CLASS RECORD · QUESTION 1

- Put each party's support in its own column, with one row per policy. - Calculate gap as Democrat minus Republican, and size as the absolute value of gap. - Put the policies with the largest size first. - Display item, Democrat, Republican, gap, and size.

73 anonymous responses

1. Other responses73

QUESTION 1 · Other responses · 1/18

Student responses

1. largest_gaps <- party_support %>% pivot_wider( names_from = party, values_from = support ) %>% mutate( gap = Democrat - Republican, size = abs(gap) ) %>% arrange(desc(size)) %>% select(item = policy, Democrat, Republican, gap, size)
2. party_support party_support |> tidyr::pivot_wider( names_from = party, values_from = support ) |> dplyr::mutate( gap = Democrat - Republican, size = abs(gap) ) |> dplyr::arrange(dplyr::desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support %>% pivot_wider(names_from = group, values_from = support) %>% mutate( gap = Democrat - Republican, size = abs(gap) ) %>% arrange(desc(size)) %>% select(item, Democrat, Republican, gap, size)
4. party_support |> dplyr::filter(group %in% c("Democrat", "Republican")) |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 2/18

Student responses

1. party_support |> filter(group != "Independent") |> pivot_wider( names_from = group, values_from = support ) |> mutate( gap = Democrat - Republican, size = abs(gap) ) |> arrange(desc(size)) |> select(item, Democrat, Republican, gap, size)
2. party_support |> pivot_wider( names_from = group, values_from = support ) |> mutate( gap = Democrat - Republican, size = abs(gap) ) |> arrange(desc(size)) |> select(item, Democrat, Republican, gap, size)
3. party_support |> pivot_wider( names_from = group, values_from = support ) |> mutate( gap = Democrat - Republican, size = abs(gap) ) |> arrange(desc(size)) |> select(item, Democrat, Republican, gap, size) |> slice(1:5)
4. party_support |> pivot_wider( names_from = party, values_from = support ) |> mutate( gap = Democrat - Republican, size = abs(gap) ) |> arrange(desc(size)) |> select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 3/18

Student responses

1. party_support |> pivot_wider(names_from = group, values_from = support) |> mutate( gap = Democrat - Republican, size = abs(gap) ) |> arrange(desc(size)) |> select(item, Democrat, Republican, gap, size)
2. party_support |> pivot_wider(names_from = group, values_from = support) |> mutate( gap = Democrat - Republican, size = abs(gap) ) |> arrange(desc(size)) |> select(item, Democrat, Republican, gap, size)
3. party_support |> pivot_wider(names_from = group, values_from = support) |> mutate(gap = Democrat - Republican, size = abs(gap)) |> arrange(desc(size)) |> select(item, Democrat, Republican, gap, size)
4. party_support |> tidyr::pivot_wider( names_from = group, values_from = support ) |> dplyr::mutate( gap = Democrat - Republican, size = abs(gap) ) |> dplyr::arrange(dplyr::desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 4/18

Student responses

1. party_support |> tidyr::pivot_wider( names_from = party, values_from = support ) |> dplyr::mutate( gap = Democrat - Republican, size = abs(gap) ) |> dplyr::arrange(dplyr::desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(dplyr::desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = (Democrat - Republican), size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = (Democrat - Republican), size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 5/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap))item) |> dplyr::arrange(desc(`Wealth tax`))
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::filter(item %in% c( "Increased deportations", "Guarantee legal abortion rights", "School sports teams by sex at birth", "Trump's tariffs" )) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 6/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 7/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 8/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 9/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 10/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 11/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(Gap = Democrat - Republican, Size = abs(Gap)) |> dplyr::arrange(desc(Size)) |> dplyr::select(item, Democrat, Republican, Gap, Size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size=abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size))
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(Democrat - Republican)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 12/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 13/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 14/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::select(item, Democrat, Republican, gap, size) |> dplyr::arrange(desc(size))
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::select(item, Democrat, Republican, gap, size) |> dplyr::arrange(desc(size))
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican) |> dplyr::arrange(desc(abs(gap)))
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican) |> dplyr::arrange(desc(gap)) |> dplyr::select(item, Democrat, Republican, gap)
5. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican) |> dplyr::mutate(size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 15/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican) |> dplyr::mutate(size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican) |> dplyr::mutate(size = abs(gap)) |> dplyr::select(item, Democrat, Republican, gap, size) |> dplyr::arrange(desc(size))
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat-Republican, size = abs(gap)) |> dplyr::arrange(desc(gap)) |> dplyr::select(item, Democrat, Republican, gap, size)
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat-Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 16/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat-Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::select(item, Democrat, Republican) |> dplyr::mutate(gap = Democrat - Republican) |> dplyr::mutate(size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate()
4. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)

QUESTION 1 · Other responses · 17/18

Student responses

1. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
2. party_support |> tidyr::pivot_wider(names_from = group, values_from = support) |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::arrange(desc(size)) |> dplyr::select(item, Democrat, Republican, gap, size)
3. party_support |> tidyr::pivot_wider(names_from = item, values_from = support)
4. party_support |> tidyr::pivot_wider(names_from = item, values_from = support) |> dplyr::mutate()
5. party_support |> tidyr::pivot_wider(names_from = item, values_from = support) |> dplyr::mutate() |> dplyr::arrange(group) |> dplyr::select(group, `Congressional stock-trading ban`, `End military aid to Israel`, `Guarantee legal abortion rights`, `Increased deportations`, `Medicare for All`, `School sports teams by sex at birth`, `Trump's tariffs`, `Voter photo ID`, `Wealth tax`)

QUESTION 1 · Other responses · 18/18

Student responses

1. party_support |> pivot_wider(id_cols=item, names_from=group, values_from=support) |> mutate(gap = Democrat - Republican, size = abs(gap)) |> arrange(desc(size)) |> select(item, Democrat, Republican, gap, size)
2. party_support |> pivot_wider(names_from = group, values_from = support) |> mutate(gap = Democrat - Republican, size = abs(gap)) |> arrange(desc(size)) |> select(item, Democrat, Republican, gap, size)
3. party_supportparty_support |> filter(group %in% c("Democrat", "Republican")) |> pivot_wider( names_from = group, values_from = support ) |> mutate( gap = Democrat - Republican, size = abs(gap) ) |> arrange(desc(size)) |> select(item, Democrat, Republican, gap, size)

CLASS RECORD · QUESTION 2

- Calculate gap as Democrat minus Republican, and size as the absolute value of gap. - Create leaning: Democrat when gap is positive, Republican otherwise. - Order the policy labels with item = reorder(item, size), so that the largest gap appears at the top. - Draw horizontal bars with size on X, policy on Y, and fill determined by leaning.

71 anonymous responses

1. Other responses71

QUESTION 2 · Other responses · 1/20

Student responses

1. ggplot2::ggplot( policy_wide |> dplyr::mutate( gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item = reorder(item, size) ), ggplot2::aes(x = size, y = item, fill = leaning) ) + ggplot2::geom_col()
2. ggplot2::ggplot( policy_wide |> dplyr::mutate( gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ), ggplot2::aes( x = size, y = item, fill = leaning ) ) + ggplot2::geom_col()
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item,size)), ggplot2::aes(x = size, y = item, fill = factor(leaning))) + ggplot2::geom_col()

QUESTION 2 · Other responses · 2/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican" ), item_ordered = reorder(item, gap)), ggplot2::aes(x = gap, y = item_ordered, fill = factor(leaning))) + ggplot2::geom_col()
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican" ), size = abs(gap), item = reorder(item, size)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col()
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item = reorder(item, size)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col() + ggplot2::scale_fill_manual(values = c(Democrat = "blue", Republican = "red"), name = NULL)
4. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item = reorder(item, size)), ggplot2::aes(x = gap, y = item)) + ggplot2::geom_col()

QUESTION 2 · Other responses · 3/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item = reorder(item, size)), ggplot2::aes(x = gap, y = item)) + ggplot2::geom_col()
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(Gap = Democrat - Republican, Leaning = ifelse(Gap > 0, "Democrat", "Republican"), item = reorder(item, Gap)), ggplot2::aes(x = Gap, y = item, fill = factor(Leaning))) + ggplot2::geom_col()
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, gap)), ggplot2::aes(x = gap, y = item)) + ggplot2::geom_col()
4. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item = reorder(item, size)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col()

QUESTION 2 · Other responses · 4/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item = reorder(item, size)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col()
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item_ordered = reorder(item, gap)), ggplot2::aes(x = gap, y = item_ordered, fill = factor(leaning))) + ggplot2::geom_col()
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item_reordered = reorder(item, size)), ggplot2::aes(x = gap, y = item_reordered, fill = factor(leaning))) + ggplot2::geom_col()
4. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item = reorder(item, size)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col()

QUESTION 2 · Other responses · 5/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap>0, 'Democrat', 'Republican'), item_ordered = reorder(item, gap)), ggplot2::aes(x = gap, y = item_ordered, fill = factor(leaning))) + ggplot2::geom_col() + ggplot2::scale_fill_manual(values = c(Democrat = 'blue', Republican = 'red'), name = NULL)
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(Gap = Democrat - Republican, Size = abs(Gap), Leaning = if_else((Gap > 0), "Democrat", "Republican"), item = reorder(item, Size)) |> dplyr::arrange(desc(Size)), ggplot2::aes(x = Size, y = item, fill = factor(Leaning))) + ggplot2::geom_col() + ggplot2::scale_fill_manual(values = c(Democrat = "blue", Republican = "red"), name = NULL)
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size)), ggplot2::aes(x = size, y = item, fill = factor(leaning))) + ggplot2::geom_col()

QUESTION 2 · Other responses · 6/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse( gap > 0, "Democrat", "Republican" ), item = reorder(item, size)), ggplot2::aes(x = size, y = item, fill = factor(leaning))) + ggplot2::geom_col() + ggplot2::scale_fill_manual(values = c(Democrat = "Blue", Republican = "Red"), name = NULL)
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item_ordered = reorder(item, gap)), ggplot2::aes(x = gap, y = item_ordered, fill = factor(leaning))) + ggplot2::geom_col()
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item,gap)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col()

QUESTION 2 · Other responses · 7/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, size)), ggplot2::aes(x = size, y = item, fill = factor(leaning))) + ggplot2::geom_col()
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, gap)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col() + ggplot2::scale_fill_manual(values = c(Democrat = "Blue", Republican = "Red"), name = NULL)
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, size)), ggplot2::aes(x = size, y = item, fill = factor(leaning))) + ggplot2::geom_col()

QUESTION 2 · Other responses · 8/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, size)), ggplot2::aes(x = size, y = item, fill = factor(leaning))) + ggplot2::geom_col()
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, size)), ggplot2::aes(x = size, y = item, fill = factor(leaning))) + ggplot2::geom_col()
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item_ordered = reorder(item, size)), ggplot2::aes(x = gap, y = item_ordered, fill = factor(leaning))) + ggplot2::geom_col()

QUESTION 2 · Other responses · 9/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::mutate(leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size)), ggplot2::aes(x = size, y = item, fill = factor(leaning))) + ggplot2::geom_col() + ggplot2::scale_fill_manual(values = c(Democrat = "blue", Republican = "red"), name = NULL)
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item = reorder(item, size)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col()
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican" ), item = reorder(item, size)), ggplot2::aes(x = size, y = item, fill = factor(leaning))) + ggplot2::geom_col()

QUESTION 2 · Other responses · 10/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, size)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col()
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, gap)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col()
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, size)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col()
4. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item_ordered = reorder(item, gap)), ggplot2::aes(x = gap, y = item_ordered, fill = factor(leaning))) + ggplot2::geom_col()

QUESTION 2 · Other responses · 11/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, Democrat, Republican), item = reorder(item, gap)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col() + ggplot2::scale_fill_manual(values = c(Democrat = "blue", Republican = "red"), name = NULL)
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap >0, "Democrat", "Republican"), item = reorder(item,gap)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col()
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap>0, "Democrat", "Republican"), item_ordered = reorder(item, gap) ), ggplot2::aes(x = gap, y = item_ordered, fill = factor(leaning))) + ggplot2::geom_col()

QUESTION 2 · Other responses · 12/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::mutate(leaning = ifelse(gap > 0, "Democrat", "Republican"), policy = reorder(item, size)), ggplot2::aes(x = size, y = policy, fill = factor(leaning))) + ggplot2::geom_col()
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::mutate(leaning=ifelse((gap>=0), "Democrat", "Republican") ) |> dplyr::mutate(item=reorder(item, size)) |> dplyr::arrange(desc(gap)), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col()
3. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican) |> dplyr::mutate(leaning = if_else(gap > 0 , "Democrat", "Republican"), size = abs(gap), item = reorder(item, size)), ggplot2::aes(x = size, y = item, fill = factor(leaning))) + ggplot2::geom_col()

QUESTION 2 · Other responses · 13/20

Student responses

1. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat - Republican) |> dplyr::mutate(size = abs(gap)) |> dplyr::mutate(leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, size)), ggplot2::aes(x = size, y = item, fill = factor(leaning))) + ggplot2::geom_col()
2. ggplot2::ggplot(policy_wide |> dplyr::mutate(gap = Democrat-Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item_ordered = reorder(item, size)), ggplot2::aes(x = gap, y = item_ordered, fill = factor(leaning))) + ggplot2::geom_col()
3. ggplot2::ggplot(policy_wide |> dplyr::select(item, Democrat, Republican) |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap>0, "Democrat", "Republican"), size = abs(gap), item = reorder(item,gap)) |> dplyr::mutate(leaning = ifelse(gap>0, "Democrat", "Republican")), ggplot2::aes(x = gap, y = item, fill = factor(leaning))) + ggplot2::geom_col() + ggplot2::scale_fill_manual(values = c(Democrat = "blue", Republican = "red"), name = NULL)

QUESTION 2 · Other responses · 14/20

Student responses

1. library(tidyverse) policy_wide |> mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()
2. policy_wide
3. policy_wide library(dplyr) library(ggplot2) policy_wide |> mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()
4. policy_wide policy_wide |> mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col() + scale_y_discrete(limits = rev)

QUESTION 2 · Other responses · 15/20

Student responses

1. policy_wide %>% mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican") ) %>% mutate(item = reorder(item, size)) %>% ggplot(aes(x = size, y = item, fill = leaning)) + geom_col() + labs( x = "Size (Absolute Gap)", y = "Policy Item", fill = "Leaning" ) + theme_minimal()
2. policy_wide %>% mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) %>% ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()
3. policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, size)) |> ggplot2::ggplot(ggplot2::aes(x = size, y = item, fill = leaning)) + ggplot2::geom_col()
4. policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican" ))

QUESTION 2 · Other responses · 16/20

Student responses

1. policy_wide |> dplyr::mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"))
2. policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::mutate(leaning = ifelse(gap > 0, Democrat, Republican)) |> dplyr::mutate(item = reorder(item, size))
3. policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"))
4. policy_wide |> dplyr::mutate(gap = Democrat - Republican) size = abs(gap) leaning = ifelse(gap>0, "Democrat", "Republican")
5. policy_wide |> dplyr::mutate(gap = Democrat - Republican) |> dplyr::mutate(leaning = if_else(gap > 0, "Democrat", "Republican"))
6. policy_wide |> dplyr::mutate(gap=Democrat-Republican, leaning=ifelse(gap >0, "Democrat","Republican"))
7. policy_wide |> mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()

QUESTION 2 · Other responses · 17/20

Student responses

1. policy_wide |> mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()
2. policy_wide |> mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()
3. policy_wide |> mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()
4. policy_wide |> mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()

QUESTION 2 · Other responses · 18/20

Student responses

1. policy_wide |> mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col() + labs( x = "Size of Support Gap", y = "Policy", fill = "Leaning" )
2. policy_wide |> mutate( gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col() + labs( x = "Party gap (percentage points)", y = "Policy", fill = "Leaning" )
3. policy_wide |> mutate(gap = Democrat - Republican, size = abs(gap), leaning = ifelse(gap > 0, "Democrat", "Republican"), item = reorder(item, size)) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()

QUESTION 2 · Other responses · 19/20

Student responses

1. policy_wide |> dplyr::mutate(gap = Democrat - Republican, size = abs(gap)) |> dplyr::mutate(leaning = if_else(gap > 0, "Democrat", "Republican")) |> dplyr::mutate(item = reorder(item, size)) |> ggplot2::ggplot() + ggplot2::geom_col(aes(x = size, y = item, fill = leaning))
2. policy_wide |> mutate(gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size)) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()
3. policy_wide |> mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item = reorder(item, size)) |> ggplot(aes(x=size, y =item, fill = leaning)) + geom_col()
4. policy_wide |> mutate(gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size)) |> ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()

QUESTION 2 · Other responses · 20/20

Student responses

1. policy_widepolicy_wide %>% mutate( gap = Democrat - Republican, size = abs(gap), leaning = if_else(gap > 0, "Democrat", "Republican"), item = reorder(item, size) ) %>% ggplot(aes(x = size, y = item, fill = leaning)) + geom_col()
2. x <- policy_wide |> mutate(gap = Democrat - Republican, leaning = ifelse(gap > 0, "Democrat", "Republican"), size = abs(gap), item = reorder(item, size)) |> arrange(desc(gap)) ggplot(x, aes(x = item, y = size, fill = leaning)) + geom_col() + coord_flip() + labs(x = "Policy", y = "Size of Gap", fill = "Leaning")

CLASS RECORD · QUESTION 3

Do policies that are popular among independents also tend to be popular among Democrats, Republicans, both parties, or neither?

67 anonymous responses

1. Other responses67

QUESTION 3 · Other responses · 1/8

Student responses

1. 0
2. 0
3. 0
4. 0
5. 0
6. 0
7. 0
8. 0
9. 0

QUESTION 3 · Other responses · 2/8

Student responses

1. 1
2. 1
3. 1
4. 1
5. 1
6. 1
7. 1
8. 1
9. 1

QUESTION 3 · Other responses · 3/8

Student responses

1. 1
2. 1
3. 2
4. 2
5. 2
6. 2
7. 2
8. 2
9. 2

QUESTION 3 · Other responses · 4/8

Student responses

1. 2
2. 2
3. 2
4. 2
5. 2
6. 2
7. 2
8. 2
9. 2

QUESTION 3 · Other responses · 5/8

Student responses

1. 2
2. 2
3. 2
4. 2
5. 2
6. 2
7. 2
8. 2
9. 2

QUESTION 3 · Other responses · 6/8

Student responses

1. 2
2. 2
3. 2
4. 2
5. 2
6. 2
7. 2
8. 2
9. 2

QUESTION 3 · Other responses · 7/8

Student responses

1. 2
2. 2
3. 2
4. 2
5. 2
6. 2
7. 2
8. 2
9. 2

QUESTION 3 · Other responses · 8/8

Student responses

1. 2
2. 3
3. 3
4. 3

CLASS RECORD · QUESTION 4

Submit one of your scatterplots. Which party shows the stronger relationship between its support percentages and independents' support percentages?

60 anonymous responses

1. Other responses60

QUESTION 4 · Other responses · 1/7

Student responses

QUESTION 4 · Other responses · 2/7

Student responses

QUESTION 4 · Other responses · 3/7

Student responses

QUESTION 4 · Other responses · 4/7

Student responses

QUESTION 4 · Other responses · 5/7

Student responses

8. democrat
9. democrat is a stronger plot

QUESTION 4 · Other responses · 6/7

Student responses

1. Democrat seems to show more of a liner trend.
2. Democrat shows a stronger relationship.
3. democrats have strong relatiobship
4. democrats show stronger relationships to independents
5. Democrats show the stronger relationship. In the Democrat plot the points line up tightly along an upward trend (correlation 0.80), so policies with high Democratic support also have high independent support.
6. Democrats show the stronger relationship. In the Democrat plot, points rise together in a clear positive trend: policies Democrats support more, Independents also tend to support more. In the Republican plot, points are scattered with no clear line, so knowing Republican support tells you little about Independent support.
7. Democrats show the stronger relationship. In this plot, as Democratic support goes up, independent support also tends to go up, with the points forming a fairly clear upward pattern. The Republican version was much more scattered, so independents' preferences track Democrats more closely.
8. I think Democrats have a stronger association.
9. Independent

QUESTION 4 · Other responses · 7/7

Student responses

1. It seems liek correlationm is more clear between democrat and ind than republican
2. Republican vs independent
3. seems to be a positive correlation between democrats and independents
4. Seems to be closer?
5. SHows Republican vs Indepneding for spevcifoc topics
6. This plot shows a possible relationship between Democrats and Independents

CLASS RECORD · QUESTION 5

- Calculate distance_D and distance_R as the absolute differences from Independent support. - Keep the policies where distance_R is smaller than distance_D. - Display each policy and its Democrat, Independent, and Republican support percentages.

72 anonymous responses

1. Other responses72

QUESTION 5 · Other responses · 1/16

Student responses

1. filtered_policies <- policy_wide %>% # 2. Calculate the absolute distance from Independent support for both parties mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) %>% # 3. Keep only policies where distance_R is smaller than distance_D filter(distance_R < distance_D) %>% # 4. Display each policy alongside Democrat, Independent, and Republican percentages select(policy, Democrat, Independent, Republican)
2. policy_wide
3. policy_wide library(dplyr) policy_wide |> mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)
4. policy_wide policies %>% mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) %>% filter(distance_R < distance_D) %>% select(policy, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 2/16

Student responses

1. policy_wide policy_wide |> mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)
2. policy_wide %>% mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) %>% filter(distance_R < distance_D) %>% select(item, Democrat, Independent, Republican)
3. policy_wide |> dplyr::mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
4. policy_wide |> dplyr::mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 3/16

Student responses

1. policy_wide |> dplyr::mutate( distance_D = abs(Democrat-Independent),distance_R = abs(Republican-Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
2. policy_wide |> dplyr::mutate(differenceD=abs(Democrat-Independent), differenceR=abs(Republican-Independent)) |> dplyr::filter(differenceR<differenceD)
3. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican, distance_D, distance_R)
4. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D)
5. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican, distance_D, distance_R)

QUESTION 5 · Other responses · 4/16

Student responses

1. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
2. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
3. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
4. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
5. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 5/16

Student responses

1. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
2. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican- Independent)) |> dplyr::filter(distance_D > distance_R) |> dplyr::select(item, Democrat, Independent, Republican)
3. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican, distance_D, distance_R)
4. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 6/16

Student responses

1. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
2. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican) ) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
3. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter((distance_R < distance_D)) |> dplyr::select(item, Democrat, Independent, Republican)
4. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
5. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D)

QUESTION 5 · Other responses · 7/16

Student responses

1. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D)
2. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D)
3. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican, distance_D, distance_R)
4. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican, distance_D, distance_R)
5. policy_wide |> dplyr::mutate(distance_d = abs(Independent - Democrat), distance_r = abs(Independent - Republican)) |> dplyr::filter(distance_r < distance_d) |> dplyr::select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 8/16

Student responses

1. policy_wide |> dplyr::mutate(distance_d = abs(Independent - Democrat), distance_r = abs(Independent - Republican)) |> dplyr::filter(distance_r < distance_d) |> dplyr::select(item, Democrat, Independent, Republican)
2. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
3. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
4. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
5. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 9/16

Student responses

1. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
2. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
3. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
4. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
5. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 10/16

Student responses

1. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
2. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
3. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
4. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
5. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R<distance_D) |> dplyr::select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 11/16

Student responses

1. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
2. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
3. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
4. policy_wide |> dplyr::mutate(distance_d = abs(Independent - Democrat), distance_r = abs(Independent - Republican)) |> dplyr::filter(distance_r < distance_d) |> dplyr::select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 12/16

Student responses

1. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
2. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
3. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
4. policy_wide |> dplyr::mutate(distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
5. policy_wide |> dplyr::mutate(distance_D = Independent - Democrat, distance_R = Independent - Republican) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 13/16

Student responses

1. policy_wide |> dplyr::mutate(distance_D= abs(Independent - Democrat), distance_R = abs(Independent - Republican) ) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
2. policy_wide |> dplyr::mutate(distance_R = abs(Republican - Independent), distance_D = abs(Democrat - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Democrat, Independent, Republican)
3. policy_wide |> mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)
4. policy_wide |> mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)
5. policy_wide |> mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 14/16

Student responses

1. policy_wide |> mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)
2. policy_wide |> mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)
3. policy_wide |> mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican) 2 * pt(-abs(-2.12), df = 33)
4. policy_wide |> mutate( distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican) ) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)
5. policy_wide |> mutate( distance_D = abs(Independent - Democrat), distance_R = abs(Independent - Republican) ) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 15/16

Student responses

1. policy_wide |> mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)
2. policy_wide |> mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)
3. policy_wide |> dplyr::mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> dplyr::filter(distance_R < distance_D) |> dplyr::select(item, Independent, Democrat, Republican)
4. policy_wide |> mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)
5. policy_wide |> mutate(distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent)) |> filter(distance_R < distance_D) |> select(item, Democrat, Independent, Republican)

QUESTION 5 · Other responses · 16/16

Student responses

1. policy_widepolicy_wide %>% mutate( distance_D = abs(Democrat - Independent), distance_R = abs(Republican - Independent) ) %>% filter(distance_R < distance_D) %>% select(item, Democrat, Independent, Republican)

CLASS RECORD · QUESTION 6

Which of these countries do you think has the highest average life satisfaction?

71 anonymous responses

1. Other responses71

QUESTION 6 · Other responses · 1/8

Student responses

1. 0
2. 0
3. 0
4. 0
5. 0
6. 0
7. 0
8. 0
9. 0

QUESTION 6 · Other responses · 2/8

Student responses

1. 0
2. 0
3. 0
4. 0
5. 0
6. 0
7. 0
8. 0
9. 0

QUESTION 6 · Other responses · 3/8

Student responses

1. 0
2. 0
3. 0
4. 0
5. 0
6. 0
7. 0
8. 0
9. 0

QUESTION 6 · Other responses · 4/8

Student responses

1. 0
2. 0
3. 0
4. 0
5. 0
6. 0
7. 0
8. 0
9. 0

QUESTION 6 · Other responses · 5/8

Student responses

1. 0
2. 0
3. 0
4. 0
5. 0
6. 0
7. 0
8. 0
9. 0

QUESTION 6 · Other responses · 6/8

Student responses

1. 0
2. 0
3. 0
4. 0
5. 0
6. 0
7. 0
8. 0
9. 0

QUESTION 6 · Other responses · 7/8

Student responses

1. 0
2. 0
3. 0
4. 0
5. 0
6. 1
7. 2
8. 2
9. 3

QUESTION 6 · Other responses · 8/8

Student responses

1. 3
2. 3
3. 3
4. 3
5. 3
6. 3
7. 3
8. 3

CLASS RECORD · QUESTION 7

Join `life_ratings` to `gdp` by `country`, keeping countries that appear in both tables. Make a scatterplot with `gdp_per_capita` on X and `happiness` on Y. Each point should represent one country. Submit your plot and describe the shape of the relationship.

65 anonymous responses

1. Other responses65

QUESTION 7 · Other responses · 1/8

Student responses

QUESTION 7 · Other responses · 2/8

Student responses

QUESTION 7 · Other responses · 3/8

Student responses

QUESTION 7 · Other responses · 4/8

Student responses

QUESTION 7 · Other responses · 5/8

Student responses

8. as GDP per capita increases, its a logarithmic increase in happiness, initial big increase, then tapers out
9. blue

QUESTION 7 · Other responses · 6/8

Student responses

1. happiness rises steeply as GDP per capita goes from low to moderate levels, then flattens out among richer countries. That’s a positive relationship that is curved (concave, roughly logarithmic) rather than linear, with a fair amount of scatter around the trend
2. Happiness tends to increase with GDP per capita, but the relationship levels off. At low incomes, happiness rises steeply as GDP increases, while among richer countries happiness is mostly between about 6 and 7.5 and does not keep rising much with extra income. So the shape looks curved rather than a straight line.
3. It looks log, like it spikes up and then flatlines
4. logarithmic
5. Logarithmic relationship/shape of the plot
6. low income countries have a lower happiness rating but once a country reaches a certain amount of income it levels out its happiness
7. Past a certain gdp per capita, gdp per capita doesn't affect happiness as much(convex). Overall, money is positively associated with happiness.

QUESTION 7 · Other responses · 7/8

Student responses

1. positive but curved (concave), with diminishing returns. Happiness climbs steeply as GDP rises across poor and middle-income countries, then flattens out among the richest, the points level off rather than continuing up at the same rate.
2. positive correlation
3. positive relationship
4. positive shape, curved?
5. Positive, nonlinear (concave) relationship. Among lower-income countries (under about $20k), happiness rises steeply, from roughly 3–5 up to about 6. Above about $25k, the curve flattens: happiness stays around 6–7.5 even as GDP per capita grows past $100k, showing diminishing returns. Poorer countries also vary much more in happiness than richer ones. A log scale on the x-axis would likely make it look more linear.
6. possibly logistic relationship
7. Somewhat relationship, but is increasing concave down line
8. The plot appears to show a relationship between higher gdp_per_capita and higher happiness
9. the plot shows a positive non-linear relationship. Happiness increases rapidly as GDP per capita rises from low to moderate level

QUESTION 7 · Other responses · 8/8

Student responses

1. There is a positive relationship between GDP per capita and happiness. Countries with higher GDP per capita generally have higher happiness scores. The relationship appears nonlinear, with happiness increasing more slowly at higher income levels.
2. There is a positive relationship between GDP per capita and happiness. Countries with higher GDP per capita tend to have higher happiness scores, but the relationship is not perfectly linear.
3. There is an initial upwards trend between gdp and happiness, but relationship doesn't appear to be linear and plateaus.
4. This is generally a positive relationship, having a higher gdp per capita generally coorelates with a higher happiness rating.

CLASS RECORD · QUESTION 8

- Fit a simple regression of happiness on income_thousands and inspect the model summary.

66 anonymous responses

1. Other responses66

QUESTION 8 · Other responses · 1/8

Student responses

1. happiness_model <- lm(happiness ~ income_thousands, data = income_data) summary(happiness_model)
2. happiness_regression <- stats::lm(happiness ~ income_thousands, data = income_data) summary(happiness_regression)
3. income_data
4. income_data income_data |> lm(happiness ~ income_thousands, data = _) |> summary()
5. income_data model <- lm(happiness ~ income_thousands, data = income_data) summary(model)
6. income_data model <- lm(happiness ~ income_thousands, data = income_data) summary(model)
7. income_data lm(formula = happiness ~ income_thousands, data = income_data)
8. income_data %>% mutate(income_thousands = gdp_per_capita / 1000) %>% lm(happiness ~ income_thousands, data = .) %>% summary()
9. income_data |> dplyr::mutate(income_thousands=gdp_per_capita/1000) |> stats::lm(happiness~income_thousands, data = _)

QUESTION 8 · Other responses · 2/8

Student responses

1. income_data |> lm(formula = happiness ~ income_thousands) |> summary()
2. income_data |> lm(happiness ~ income_thousands, data = _)
3. income_data |> lm(happiness ~ income_thousands, data = _)
4. income_data |> lm(happiness ~ income_thousands, data = _) |> summary()
5. income_data |> lm(happiness ~ income_thousands, data = _) |> summary()
6. income_data |> mutate(income_thousands = gdp_per_capita / 1000) |> lm(happiness ~ income_thousands, data = _) |> summary()
7. income_data |> stats::lm(happiness = income_thousands, data = _)
8. income_data |> stats::lm(happiness ~ gdp_per_capita, data = _)
9. income_data |> stats::lm(happiness ~ gdp_per_capitaincome_thousands, data = _)

QUESTION 8 · Other responses · 3/8

Student responses

1. income_data |> stats::lm(happiness ~ income_thousands, data = _)
2. income_data |> stats::lm(happiness ~ income_thousands, data = _)
3. income_data |> stats::lm(happiness ~ income_thousands, data = _)
4. income_data |> stats::lm(happiness ~ income_thousands, data = _)
5. income_data |> stats::lm(happiness ~ income_thousands, data = _)
6. income_data |> stats::lm(happiness ~ income_thousands, data = _)
7. income_data |> stats::lm(happiness ~ income_thousands, data = _)
8. income_data |> stats::lm(happiness ~ income_thousands, data = _)
9. income_data |> stats::lm(happiness ~ income_thousands, data = _)

QUESTION 8 · Other responses · 4/8

Student responses

1. income_data |> stats::lm(happiness ~ income_thousands, data = _)
2. income_data |> stats::lm(happiness ~ income_thousands, data = _)
3. income_data |> stats::lm(happiness ~ income_thousands, data = _)
4. income_data |> stats::lm(happiness ~ income_thousands, data = _)
5. income_data |> stats::lm(happiness ~ income_thousands, data = _)
6. income_data |> stats::lm(happiness ~ income_thousands, data = _)
7. income_data |> stats::lm(happiness ~ income_thousands, data = _)
8. income_data |> stats::lm(happiness ~ income_thousands, data = _)
9. income_data |> stats::lm(happiness ~ income_thousands, data = _)

QUESTION 8 · Other responses · 5/8

Student responses

1. income_data |> stats::lm(happiness ~ income_thousands, data = _)
2. income_data |> stats::lm(happiness ~ income_thousands, data = _)
3. income_data |> stats::lm(happiness ~ income_thousands, data = _)
4. income_data |> stats::lm(happiness ~ income_thousands, data = _)
5. income_data |> stats::lm(happiness ~ income_thousands, data = _)
6. income_data |> stats::lm(happiness ~ income_thousands, data = _)
7. income_data |> stats::lm(happiness ~ income_thousands, data = _)
8. income_data |> stats::lm(happiness ~ income_thousands, data = _)
9. income_data |> stats::lm(happiness ~ income_thousands, data = _)

QUESTION 8 · Other responses · 6/8

Student responses

1. income_data |> stats::lm(happiness ~ income_thousands, data = _)
2. income_data |> stats::lm(happiness ~ income_thousands, data = _)
3. income_data |> stats::lm(happiness ~ income_thousands, data = _)
4. income_data |> stats::lm(happiness ~ income_thousands, data = _)
5. income_data |> stats::lm(happiness ~ income_thousands, data = _)
6. income_data |> stats::lm(happiness ~ income_thousands, data = _)
7. income_data |> stats::lm(happiness ~ income_thousands, data = _)
8. income_data |> stats::lm(happiness ~ income_thousands, data = _)
9. income_data |> stats::lm(happiness ~ income_thousands, data = _)

QUESTION 8 · Other responses · 7/8

Student responses

1. income_data |> stats::lm(happiness ~ income_thousands, data = _)
2. income_data |> stats::lm(happiness ~ income_thousands, data = _) |> summary()
3. income_data |> stats::lm(happiness~income_thousands, data = income_data)
4. income_data |> stats::lm(income_thousands ~ happiness, data = _)
5. income_model <- lm( happiness ~ income_thousands, data = income_data ) summary(income_model)
6. income_model <- lm( happiness ~ income_thousands, data = income_data ) summary(income_model)
7. income_model <- lm( happiness ~ income_thousands, data = income_data ) summary(income_model)
8. lm(happiness ~ income_thousands, data = income_data) |> summary()
9. model <- lm(happiness ~ income_thousands, data = income_data) summary(model)

QUESTION 8 · Other responses · 8/8

Student responses

1. model <- lm(happiness ~ income_thousands, data = income_data); summary(model)
2. model_raw <- lm(happiness ~ income_thousands, data = income_data) summary(model_raw)
3. x <- lm(happiness ~ income_thousands, data = income_data) summary(x)

CLASS RECORD · QUESTION 9 · 1/2

Look at the scatterplot and fitted line. Where does the line tend to predict too much happiness? Where does it predict too little?

71 anonymous responses

1. Overpredicts for highs, under for lows23Respondent says the line predicts too much happiness at high income values and too little at low incomes.
2. Prediction error relative to zero point11Respondent refers to accuracy above/below zero or a baseline value rather than income-based misprediction patterns.
3. Overpredicts for middle, under for extremes8Respondent believes the line overestimates happiness for middle-income countries and underestimates it for both very low and very high incomes, implying a curved relationship.
4. Inaccurate mainly at tails6Respondent notes the model is inaccurate primarily at the extreme low or high ends of income (tails) without specifying direction.

CLASS RECORD · QUESTION 9 · 2/2

Response themes for question 9

1. Overpredicts at low, under at high6Respondent specifically says the line predicts too much happiness at low incomes and too little at high incomes.
2. Unclear or single-word response6Response too brief or unclear to assign to a substantive misprediction pattern.
3. Under for lower, over for higher incomes (swapped)6Respondent states the line underpredicts at lower incomes and overpredicts at higher incomes (opposite of some others).
4. Underpredicts for highs, over for lows5Respondent says the line predicts too little happiness for high-income observations and too much for low-income ones.

QUESTION 9 · Overpredicts for highs, under for lows · 1/3

Student responses

1. At high amounts of gdp per capita, the model overpredicts happiness. At low amounts it underpredicts
2. it over predicates happiness for wealthy countries and under predicates poorer countries
3. It predicts slightly less happiness below 75 GDP per person and slightly too much above 75 GDP per person.
4. It predicts too much happiness for high income, too little for low income
5. it predicts too much happiness for the lowest incomes and too little for the highest
6. It predicts too much happiness when the income is high, and too little happiness when the income is low
7. Its predicts too little happiness for the lowest income countries and too much happiness for the highest income countries.
8. once income is high, the line overpredicts happiness and line underestimates happiness in lowest income
9. overpredicts for high income countries

QUESTION 9 · Overpredicts for highs, under for lows · 2/3

Student responses

1. overpredicts how happy you are later in the scatterplot (richer) and underpredicts early on (less rich)
2. Tends to predict too much happiness when there is a high gdp per person. It predicts too low when the gdp per person is lower
3. the higher gdp, the more the line tend to predict too much happiness
4. The line overestimates happiness at the highest incomes and underestimates it at the mid to low incomes.
5. The line predicts too much happiness toward higher GDPs, overestimating the happiness in that region.
6. The line predicts too much happiness when income increases towards the end of the right side of the scatterplot and the model predicts too little happiness when income is smaller, or the left side of the scatterplot.
7. The line tend to predict too mush for very high GDP population and little for lower GDP
8. the line tends to predict too much happiness at higher gdp levels, and too little at low gdp levels.
9. The line tends to predict too much happiness for higher levels of income, while it predicts too little for low levels of income.

QUESTION 9 · Overpredicts for highs, under for lows · 3/3

Student responses

1. The regression line overpredicts happiness at a high gdp per capita. It predicts too little happiness for a lower gdp per capita.
2. to much happiness at 150 thousand and to little at 0
3. too much at high gdp, to little at low gdp
4. too much on the highs, too little on the lows
5. when the income is very high, the line tend to predict too much happiness

QUESTION 9 · Prediction error relative to zero point · 1/2

Student responses

1. Around a gpd per capita of 25000 has lots of data with a postiive trend, but data after does not show a strong positive trend as before
2. becomes less accurate as gdp increases
3. Fittness is too low
4. For low income(gdp), it predicts lower, and for high income countries, it predicts higher
5. it over predicts at the high end of gdp and under predicts in the lower end and middle.
6. It predicts too high when income is higher and too low when income is lower so towards the ends
7. Predicts too much above 5.0, too little below 5.0
8. The fitted line overpredicts happiness at higher GDPs and underpredicts at lower GDPs around 25,000.
9. The line predicts too little happiness at lower GDPs, while predicting too much happiness at higher GDPS

QUESTION 9 · Prediction error relative to zero point · 2/2

Student responses

1. The line predicts too much happiness when the points are too below the line
2. The poorest countries (near $0–5k). Many sit at happiness 3–4.5, below where the line starts. The richest countries (above about $80k). The line keeps climbing, but their actual happiness levels off around 6.5–7. Lower-middle and middle-income countries (about $10k–50k). Happiness has already jumped to about 6–7, faster than the line rises.

QUESTION 9 · Overpredicts for middle, under for extremes · 1/1

Student responses

1. over predicts happiness in the richest and poorest countries, under predicts in countries around 25-50 thousand GDP per capita
2. overpredicted at lower and higher gdp. predicts too little in the middle
3. The line predicts too much happiness at lower incomes and higher incomes, but it predicts too little in middle incomes because its a logarithmic curve
4. the line tends to predict too much happiness for countries with middle incomes and too little happiness for countries with very low or very high incomes, maybe c urve
5. The line tends to predict too much happiness for very low- and very high-income countries, and too little happiness for countries in the middle, because the relationship is curved rather than perfectly linear.
6. The line tends to underestimate happiness for some middle-income countries and overestimate happiness for some high-income countries. The relationship is not perfectly linear.
7. to much happiness in the middle to little at the end
8. too much happenies is seen at both ends. Too little happenies is seen towards the middle

QUESTION 9 · Inaccurate mainly at tails · 1/1

Student responses

1. at the tails
2. I think it is least accurate as you get more money as the real lline flatlines, so when you have a lot of money the happiness goes down, but if you dont have that much money, smaller increases and decreases are pretty constant.
3. Predicts too much happiness for very rich countries and too little for rather mid to poorer countries
4. the ends
5. The line predicts too much happiness for the richest countries, where most points sit below the line at high GDP per person. It predicts too little in the middle, roughly $10,000 to $30,000 per person, where many countries sit above the line. That pattern is what you would expect if the real relationship flattens out instead of staying straight.
6. When having high income happier, too little income little happiness

QUESTION 9 · Overpredicts at low, under at high · 1/1

Student responses

1. It predicts to much happiness at low gdp, and too little at high gdp
2. It predicts too little happiness for middle ground GDP and too much happiness for lower GDP
3. predicts too mich at the beggining and too little at thr end
4. the line predicts too much happiness at lower gdp and less at higher gdp
5. underpredict low and vice versa
6. We overpredict happiness at very low GDP, and under predict at middling levels of GDP

QUESTION 9 · Unclear or single-word response · 1/1

Student responses

1. at the end of the line
2. income
3. people with too much wealthy are not happier
4. The line tends to predict too much happiness for very high-income countries and too little happiness for some middle-income countries.
5. too much happiness at low GDP
6. When gdp is small.

QUESTION 9 · Under for lower, over for higher incomes (swapped) · 1/1

Student responses

1. Between 10 and 50 thousand, the model tends to underpredict happiness. Upwards of 50 thousand it tends to more frequently overpredict happiness
2. It predicts too little towards a large x
3. The line predicts too little at the beginning of the scatterplot, while it predicts too much towards the end with the richer countries.
4. the line predicts too much for richer,and it predicts too little around 30 gdp per capita
5. under predict for lower incomes and over predict for higher incomes
6. Underpredicts happiness in the first 25 thousands. And overpredicts in the top GDP countries.

QUESTION 9 · Underpredicts for highs, over for lows · 1/1

Student responses

1. The graph when its towards the end overpredicts happiness because at one point there isn't enough data points to justify our prediction
2. The line predicts too little happiness for low- and middle-income countries. The line predicts too much happiness for the richest countries.
3. The linear model tends to overpredict happiness for countries with very low GDP per capita and for countries with very high GDP per capita.
4. We tend to overpredict ahppaines for poor people and underpredict happiness for rich people
5. We tend to overpredict happiness at lower x values and underpredict at higher x values.

CLASS RECORD · QUESTION 10

Make a scatterplot with `fitted` on X and `resid` on Y. Submit your plot. Do you see a pattern?

64 anonymous responses

1. Other responses64

QUESTION 10 · Other responses · 1/8

Student responses

QUESTION 10 · Other responses · 2/8

Student responses

QUESTION 10 · Other responses · 3/8

Student responses

QUESTION 10 · Other responses · 4/8

Student responses

QUESTION 10 · Other responses · 5/8

Student responses

QUESTION 10 · Other responses · 6/8

Student responses

9. As fitted increases, residual decreases.

QUESTION 10 · Other responses · 7/8

Student responses

1. es, there’s a clear arch (upside-down U) instead of random scatter around 0. At the lowest fitted values (about 4.9), residuals are mostly negative, down to about -3.5. Around fitted 5.2–5.6 they’re mostly positive (up to about +1.8). At the highest fitted values (above 7), they’re all negative (about -0.4 to -2.2). So the line overpredicts happiness for the poorest and richest countries and underpredicts it for middle-income ones. The spread is also widest at low fitted values. Both patterns me
2. It is inversely proportional. Resid vs fitted
3. Resid tend to be larger when happiness level increases
4. The residuals vs fitted values plot shows some relationship between the two - which implies that the true model is not linear
5. there is a curved pattern
6. There is a parabolic shape
7. wowza, more negative as we go onward
8. yes

QUESTION 10 · Other responses · 8/8

Student responses

1. Yes, I see a pattern. The residuals are mostly positive in the middle, around fitted values of about 5.2 to 5.6, and mostly negative at low and especially at high fitted values. That curved shape suggests the straight line is missing the flattening relationship, rather than the residuals just scattering randomly around zero.
2. yes, there’s a clear pattern. The residuals aren’t a flat, formless band around zero, they curve. They start low, rise to positive in the middle range of fitted values, then bend back down and go negative at the high end.

CLASS RECORD · QUESTION 11 · 1/2

Would an extra $10,000 of GDP per person have the same relationship with happiness in a country with GDP per person of $5,000 as in one with $80,000? What feature of our plot supports your answer?

75 anonymous responses

1. Effect differs: diminishing returns43Students state that an extra $10,000 has a larger positive association with happiness in poorer countries than in richer ones, citing the plot’s curved pattern that flattens at higher incomes.
2. Says effect differs without detail10Student indicates the effect is not the same but gives no supporting reasoning or plot feature.
3. Says effect is the same5Student asserts the relationship would be the same across low- and high-income countries.
4. Notes fitted line decreases at high income4Student observes the fitted line initially rises but then falls at higher GDP, implying a different pattern than simple flattening.

CLASS RECORD · QUESTION 11 · 2/2

Response themes for question 11

1. References utility function or concave regression4Student invokes economic utility shapes or concave regression (e.g., square-root utility, concave fitted line) to explain diminishing marginal benefit of income.
2. States poorer person would be happier with increase4Student directly asserts that the person in the poorer country would experience more happiness from the extra $10,000, without referencing plot shape or fitted line.
3. Mentions parabolic/peak pattern3Student says the plot shows a peak or parabola so increases at very high incomes might decrease happiness, implying a non-monotonic relationship.
4. Other responses2

QUESTION 11 · Effect differs: diminishing returns · 1/8

Student responses

1. _..._ .::::. `. * :::::::. : :::::::: : . `::::::' .' `'::'-' * * /\ /\ / \ /\ / \ /____\ / \ /____\ | [] | /____\ | [] | | | | [] | |____| |____| |____| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2. An extra 10000 GDP would more greatly benefit people in country with lower GDP as the model is fairly accurate
3. I think an extra 10,000 would have a higher impact on happiness in a country with 5,000 GDP per person because happiness sees a drastic increase toward the beginning of the plot
4. If you already have more money, the additional income would not increase happiness as much as in a lower GDP country.
5. no
6. No Diminishing returns / concave curve: The relationship between GDP per person and happiness is logarithmic/concave rather than linear. The slope is very steep
7. No it would have a much larger impact on the country with $5000 than $80000
8. no it would not be the same. 10000 would increase happiness much more for countries with a low gdp than if a high gdp country got that extra money
9. No the curve shows diminishing returns

QUESTION 11 · Effect differs: diminishing returns · 2/8

Student responses

1. No the extra money would have a different impact on the two countries because that added money can be more valuable based on the current income each country already makes
2. No you dont got as happy as the person making 5,000. the feature thatll support this is that the curve flattens at the end
3. No, an extra $10,000 does not have the same relationship with happiness in both countries because in lower income countries an extra $10,000 represents a massive percentage increase in resources.
4. No, an extra $10,000 would probably have a bigger relationship with happiness in a lower-income country than in a higher-income country, because the plot curves and starts to flatten as GDP per person gets higher.
5. No, as GDP increases, there are diminishing returns. This is explained because our graph is not linear
6. No, as the plot is nonlinear. The marginal utility is higher for people who don't make as much money.

QUESTION 11 · Effect differs: diminishing returns · 3/8

Student responses

1. No, because according to the theory of diminishing marginal utility, if the person with $80,000 gets an additional 10,000 dollars, they are not as happy as the person who started with 5000 dollars and received an additional 10,000
2. no, because the relationship is not linear, as demonstrated by the better fit line being nonlinear. 10k of GDP would have a significantly larger effect at 5k than 80k.
3. No, because the scatterplot rises steeply at low GDP and flattens at high GDP, an extra $10,000 matters much more to a country at $5,000 than to one at $80,000.
4. No, I don't think the effect is the same. An extra $10,000 would likely have a bigger relationship with happiness in a poorer country than in a richer country. The scatterplot shows that happiness increases quickly at lower GDP levels, but the increase becomes smaller at higher GDP levels.

QUESTION 11 · Effect differs: diminishing returns · 4/8

Student responses

1. No, I don't think the effect would be the same. An extra $10,000 would likely make a bigger difference in happiness for a country with $5,000 GDP per person than one with $80,000. The scatterplot shows a curved relationship that starts to flatten at higher incomes. This suggests that happiness increases with income, but the increase gets smaller as countries become wealthier.
2. No, increasing the amount of money at higher GDP has diminishing returns because the relationship is not linear. There is a horizontal asymptote of happiness.
3. No, it is like diminishing marginal returns in econ, because if you have less you are more happy
4. no, it would give more happiness to ppl with less gdp, as we can see by the hgiher residual there
5. No, it would not be the same. An extra $10,000 would matter much more at $5,000 per person, where happiness rises steeply with income, than at $80,000, where the curve has flattened out. The scatterplot supports this because happiness climbs quickly at low GDP per person and then levels off among richer countries.
6. No, probably more for 5000

QUESTION 11 · Effect differs: diminishing returns · 5/8

Student responses

1. No, the 10,000s has a different impact depending on the local economy. The poor fit of the linear model supports my answer, we should use a non-linear model instead
2. no, the changing slope for different x supports this answer, it's not exactly linear from the plot
3. No, the curve in the plot
4. No, the extra 10,000 would be much more impactful for the country with a gdp of 5,000
5. No, the extra GDP would be more significant for individuals with 5000 average GDP. The plots whows that the model is not linear
6. No, the plot is nonlinear meaning the marginal utility is different depending on how much money you have
7. No, the regression line is concave down which indicates diminishing marginal benefit to each dollar earned.
8. no, the slope of the plot at different points shows that increasing the gdp by 10,000 for different countries with different current gdps would have different effects
9. No, with lower income, the relationship with happiness is more linear, but with higher income, the relationship with happiness is more flat.

QUESTION 11 · Effect differs: diminishing returns · 6/8

Student responses

1. No! This does not mean the same to all. The benefit of the additonal good is not as high for the person that already has a higher GDP per person. We see this by the model showing it constantly thinking higher income countries will be happier than they actually are and lower income countries with less happiness than observed
2. No. An additional $10,000 in GDP per capita appears to be associated with a larger increase in happiness for lower-income countries than for higher-income countries.
3. No. An extra $10,000 buys much more happiness at $5,000 than at $80,000.The feature that supports this is the curvature of the scatterplot.
4. No. An extra $10,000 of GDP per person is likely to have a larger positive relationship with happiness in a poorer country than in a richer country. The scatterplot shows a curved pattern that becomes flatter at higher income levels, suggesting diminishing returns to income.

QUESTION 11 · Effect differs: diminishing returns · 7/8

Student responses

1. No. In the scatterplot, going from about $5k to $15k is linked to happiness rising from roughly 4.5 to about 6, a gain of about 1.5 points. Going from $80k to $90k shows almost no change, since happiness is already flat around 7. The feature that shows this is the curve’s shape: it’s steep at low incomes and flattens at high incomes (diminishing returns). The residual plot backs this up, because its arch shows a single straight-line slope can’t describe both ends. This suggests happiness relates
2. No. We see this in the negative residual in high income countries. The same increase in income appears to contribute less to their happiness than for low income countries.
3. The 15k will have a larger increase in happiness than the 90k. Our line shouldnt be linear because as GDP increase the rate at which happiness is increasing decreases.
4. The extent of the person who earn $80,000 will be less happy than the one who earn less

QUESTION 11 · Effect differs: diminishing returns · 8/8

Student responses

1. utility functions are usually square root of consumption so increase in already high consumptions have a much smaller effect than the same jump when your consumption is already low

QUESTION 11 · Says effect differs without detail · 1/2

Student responses

1. I think no? Not really sure what the question is saying. I think the plot shows a inverse relationship that the lower negative residual tends to be vad. Residual is happiness - fitted, so being overpredicted makes you sadded
2. it would have a different relationship because of scale
3. no
4. No
5. no it would not have the same relationship
6. No it wouldny be the same, that amount of money is different for each of teh situations in that 10k is much more a help to teh 5k gdp than the 80k gpd population
7. no, it would affect the person of 5000 gdp more than the 80000, since it is a greater relative increase
8. No. There is no linear relationship because income would matter less in a country with $80k versus 5k
9. not the same

QUESTION 11 · Says effect differs without detail · 2/2

Student responses

1. Probably, not, since different countries would have different standard of living.

QUESTION 11 · Says effect is the same · 1/1

Student responses

1. it would negatively impact
2. No, GDP is viewed differently in different countries depending on their utlity.
3. No, since there would still be the added affects of living in that country versus the other. Independent
4. yes
5. yes

QUESTION 11 · Notes fitted line decreases at high income · 1/1

Student responses

1. According to our graph, happiness may increase but then decrease as the GDP goes higher.
2. It would not have the same relationship because our fitted line indicates that as GDP increases happiness decreases.
3. It would not have the same relationship because the slope would be positive for the first group but could be negative for the second group.
4. No. Because the fitted line increases first, then decreases.

QUESTION 11 · References utility function or concave regression · 1/1

Student responses

1. 1
2. Its different the fittingline support this
3. no the wealthy diff is too big the guy with 5000 would only feel pain living around with other rich guy
4. No, if you have 5000, the plot is good at showing the relationship with happiness, but not with higher incomes

QUESTION 11 · States poorer person would be happier with increase · 1/1

Student responses

1. It would not have the same relationship because the plot shows that happiness is more associated with income at lower income levels.
2. No, because there is a bigger increase among rthe person with less money
3. No, the person in the poorer country would give more happiness, the model is not linear
4. The person with 5,000 would be happier with the 10,000. In our plot we see bigger residuals at lower incomes

QUESTION 11 · Mentions parabolic/peak pattern · 1/1

Student responses

1. Absolutely not. Adding $10,000 to a country with a lower GDP benefits way more according to our non-linear model than a higher GDP country, which could arguably become less happy as a result based on our model!
2. No it would not, there is a clear parabolic peak of happiness at a certain GDP, so at 5,000 increasing money will increase happiness but at 80,000 increasing money will actually decrease happiness
3. No, having a different GDP per person will end up having different relationship with happiness based on the residual plot as it shows a trend of going down after a specific income

CLASS RECORD · QUESTION 12

- Fit happiness on income_doublings. Inspect the slope and R-squared in the model summary.

64 anonymous responses

1. Other responses64

QUESTION 12 · Other responses · 1/8

Student responses

1. #income_data |> lm(formula = happiness ~ income_doublings, data = income_data)
2. check_income <- stats::lm(happiness ~ income_doublings, data = income_data) summary(check_income)
3. income_data income_fit2 <- lm( happiness ~ income_doublings, data = income_data ) summary(income_fit2)
4. income_data model <- lm(happiness ~ income_doublings, data = income_data) summary(model)
5. income_data %>% lm(happiness ~ income_doublings, data = .) %>% summary()
6. income_data |> lm(formula = happiness ~ income_doublings) |> summary()
7. income_data |> lm(happiness ~ income_doublings, data = _) |> summary()
8. income_data |> lm(happiness ~ income_doublings, data = _) |> summary()
9. income_data |> lm(happiness ~ income_doublings, data = _) |> summary()

QUESTION 12 · Other responses · 2/8

Student responses

1. income_data |> lm(happiness ~ income_doublings, data = _) |> summary()
2. income_data |> lm(happiness ~ income_doublings, data = df) To inspect the slope and R-squared: income_data |> lm(happiness ~ income_doublings, data = df) |> summary()
3. income_data |> lm(happiness ~ income_doublings, data = income_data) |> summary()
4. income_data |> lm(happiness ~ income_doublings) |> summary()
5. income_data |> stats::lm(happiness ~ income_doublings , data = _)
6. income_data |> stats::lm(happiness ~ income_doublings, data = _)
7. income_data |> stats::lm(happiness ~ income_doublings, data = _)
8. income_data |> stats::lm(happiness ~ income_doublings, data = _)
9. income_data |> stats::lm(happiness ~ income_doublings, data = _)

QUESTION 12 · Other responses · 3/8

Student responses

1. income_data |> stats::lm(happiness ~ income_doublings, data = _)
2. income_data |> stats::lm(happiness ~ income_doublings, data = _)
3. income_data |> stats::lm(happiness ~ income_doublings, data = _)
4. income_data |> stats::lm(happiness ~ income_doublings, data = _)
5. income_data |> stats::lm(happiness ~ income_doublings, data = _)
6. income_data |> stats::lm(happiness ~ income_doublings, data = _)
7. income_data |> stats::lm(happiness ~ income_doublings, data = _)
8. income_data |> stats::lm(happiness ~ income_doublings, data = _)
9. income_data |> stats::lm(happiness ~ income_doublings, data = _)

QUESTION 12 · Other responses · 4/8

Student responses

1. income_data |> stats::lm(happiness ~ income_doublings, data = _)
2. income_data |> stats::lm(happiness ~ income_doublings, data = _)
3. income_data |> stats::lm(happiness ~ income_doublings, data = _)
4. income_data |> stats::lm(happiness ~ income_doublings, data = _)
5. income_data |> stats::lm(happiness ~ income_doublings, data = _)
6. income_data |> stats::lm(happiness ~ income_doublings, data = _)
7. income_data |> stats::lm(happiness ~ income_doublings, data = _)
8. income_data |> stats::lm(happiness ~ income_doublings, data = _)
9. income_data |> stats::lm(happiness ~ income_doublings, data = _)

QUESTION 12 · Other responses · 5/8

Student responses

1. income_data |> stats::lm(happiness ~ income_doublings, data = _)
2. income_data |> stats::lm(happiness ~ income_doublings, data = _)
3. income_data |> stats::lm(happiness ~ income_doublings, data = _)
4. income_data |> stats::lm(happiness ~ income_doublings, data = _)
5. income_data |> stats::lm(happiness ~ income_doublings, data = _)
6. income_data |> stats::lm(happiness ~ income_doublings, data = _)
7. income_data |> stats::lm(happiness ~ income_doublings, data = _)
8. income_data |> stats::lm(happiness ~ income_doublings, data = _) |> summary()
9. income_data |> stats::lm(happiness ~ income_doublings, data = ...)

QUESTION 12 · Other responses · 6/8

Student responses

1. income_data |> stats::lm(happiness ~ income_doublings, data = income_data, data = _)
2. income_data |> stats::lm(happiness ~ income_doublings, data = income_data)
3. income_data |> stats::lm(happiness ~ income_doublings, data = income_data)
4. income_data |> stats::lm(happiness ~ income_doublings, data = income_data)
5. income_data |> stats::lm(happiness ~ income_doublings, dataset = income_data, data = _)
6. income_data |> stats::lm(happiness ~ income_doublings)
7. income_data |> stats::lm(happiness~income_doublings, data = _)
8. income_fit <- lm( happiness ~ income_doublings, data = income_data ) summary(income_fit)
9. income_fit <- lm(happiness ~ income_doublings, data = income_data) summary(income_fit)

QUESTION 12 · Other responses · 7/8

Student responses

1. income_fit_log <- lm( happiness ~ income_doublings, data = income_data ) summary(income_fit_log)
2. income_fit_log <- lm( happiness ~ income_doublings, data = income_data ) summary(income_fit_log)
3. income_fit2 <- lm( happiness ~ income_doublings, data = income_data ) summary(income_fit2)
4. lm( happiness ~ income_doublings, data = income_data )
5. lm(happiness ~ income_doublings, data = income_data)
6. lm(happiness~income_doublings, data = income_data)
7. model <- lm(happiness ~ income_doublings, data = income_data) coef(model)["income_thousands"] summary(model)$r.squared
8. model <- lm(happiness ~ income_doublings, data = income_data); summary(model)
9. model_doublings <- lm(happiness ~ income_doublings, data = income_data) summary(model_doublings)

QUESTION 12 · Other responses · 8/8

Student responses

1. summary(lm(happiness ~ income_doublings, data = income_data))

CLASS RECORD · QUESTION 13 · 1/2

According to this line, how much higher is the predicted life rating when income per person doubles? Explain using the fitted coefficient.

67 anonymous responses

1. About 0.55 with more precision13Respondents state the coefficient value more precisely (around 0.5476 or 0.548) and connect it to the increase per doubling.
2. Around 0.54 increase12Respondents interpret the fitted coefficient as implying about a 0.54 increase in predicted life rating when income doubles.
3. Around 0.55 increase9Respondent interprets the fitted coefficient as implying about a 0.55 increase in predicted life rating when income doubles.
4. Precise 0.5476 statement8Explicit single-sentence statements asserting the predicted increase equals 0.5476 when income doubles.

CLASS RECORD · QUESTION 13 · 2/2

Response themes for question 13

1. About 1 or ~5 (overestimates)7Responses that give rounded or larger estimates (around 1 or about 5), indicating overestimation relative to the fitted coefficient.
2. Other responses7Responses the model could not place reliably.
3. Interprets as 54 increase (scale confusion)6Respondent gives 54, indicating a likely confusion about decimal placement or coefficient scaling rather than the small fractional increase.
4. Negative five (sign confusion)5Respondent states a negative five, indicating confusion about the sign or magnitude of the coefficient.

QUESTION 13 · About 0.55 with more precision · 1/2

Student responses

1. 0.5476
2. 0.5476111
3. 0.5476111 higher every time the gdp doubles
4. 0.5476111, which means the rating is about 0.54 higher when income doubles
5. fitted coefficient for income_doublings is 0.5476, meaning each doubling of income is associated with a 0.55-point increase in predicted happiness
6. It increases by an average of 0.548 points.
7. Since the coefficient on income_doublings is 0.548, each doubling of income per person raises the predicted life rating by about 0.55 points
8. THe predicted life rate is 0.548 higher when income per person doubles.
9. The predicted life rating is 0.55 points higher (or approximately 0.548 points higher) each time income per person doubles

QUESTION 13 · About 0.55 with more precision · 2/2

Student responses

1. When GDP per capita doubles, the predicted life satisfaction rating increases by approximately 0.548 points.
2. When income doubles, predicted life rating increases by 0.548
3. When income per person doubles, the predicted life rating increases by approximately 0.548 points. This is because the fitted coefficient for income_doublings is 0.5476111, meaning each additional doubling of income is associated with a 0.548-point increase in predicted happiness.
4. When income per person doubles, the predicted life satisfaction rating increases by about 0.55 points, because the slope for income_doublings is 0.5476.

QUESTION 13 · Around 0.54 increase · 1/2

Student responses

1. .54
2. .54
3. .54 higher
4. 0.54
5. 0.54
6. 0.54
7. It is higher by .54 points when income doubles.
8. Life rating is .54 higher when income doubles.
9. The predicted life rating increases by 0.54

QUESTION 13 · Around 0.54 increase · 2/2

Student responses

1. When income doubles, predicted life is higher by 0.54 years.
2. When income doubles, the predicted life rating is 0.54 points higher
3. When income per person doubles, the predicted life rating increases by 0.54

QUESTION 13 · Around 0.55 increase · 1/1

Student responses

1. .5
2. .5 points more
3. .55
4. 0.5 points higher.
5. 0.55
6. Each time income doubles, life ratings increases by about 0.55 points
7. income per person doubles, the model predicts life rating to be about 0.55 points higher
8. it goes up by 0.55 every time income doubles per person explained by out model
9. the predicted life rating increases by 0.55 when income doubles.

QUESTION 13 · Precise 0.5476 statement · 1/1

Student responses

1. 0.5476 values of happiness
2. 0.5476111 happiness units (slope of the fit)
3. Increases by 0.547 every time income doubles.
4. It would be .547, that's the coefficient for income_doublings
5. The life rating is predicted to be 0.5476 higher when the income per person doubles.
6. The predicted life rating is higher by 0.5476.
7. when income per person doubles the predicted life rating increases by about .547
8. When income per person doubles, the predicted life rating goes up by 0.5476 when compared to the average life rating

QUESTION 13 · About 1 or ~5 (overestimates) · 1/1

Student responses

1. ~5
2. 0.54. it is this much higher becasue fo the coefficient
3. 1.8
4. 5.26+0.54
5. about 0.54 pts, meaning a double in income leads to 1/2 pt increase in happiness
6. Basically you increase by half a life point
7. When income per person doubles, predicted life rating increases by about 5.27

QUESTION 13 · Other responses · 1/1

Student responses

1. 0.548
2. 0.55 * income_doublings + 5.27, every 0.55 point rise in life rating would clarify the prediction better.
3. About .54 increase
4. About 0.55 points higher on the 0 to 10 scale. The fitted line is happiness = 5.267 + 0.5476 × income_doublings. The predictor income_doublings counts how many times income has doubled from $10,000, so increasing it by 1 means income per person doubles. The slope is the change in predicted happiness per one-unit increase in the predictor, so each doubling of income raises the predicted life rating by the slope, 0.5476, roughly half a point.
5. about half as high
6. less than 1 point
7. When the income per person doubles, the predicted life rating increases by 1/2 of the previous value

QUESTION 13 · Interprets as 54 increase (scale confusion) · 1/1

Student responses

1. .5
2. 0.548
3. much higher
4. The predicted life rating increases by about 0.55 points when income per person doubles, because the fitted coefficient for `income_doublings` is 0.5476.
5. The slope coefficient for income_doublings is 0.5476111. Because the predictor variable income_doublings tracks the number of times income doubles. A 1-unit increase in income_doublings represents a doubling of GDP per person
6. When GDP per person doubles, the predicted life rating increases by about 0.55 points. This is because the slope coefficient for income_doublings is 0.5476.

QUESTION 13 · Negative five (sign confusion) · 1/1

Student responses

1. -5
2. 0.547
3. 0.5476
4. it increases by 0.547, or half a point, via the coef
5. Predicted life rating (or happiness?) is a half-year longer when income doubles. へ ♡ ╱|、 ૮ - ՛ ) (` - 7 / ⁻ ៸| |、⁻〵 乀 (ˍ, ل ل じしˍ,)ノ

CLASS RECORD · QUESTION 14

- Calculate absolute_resid as abs(resid). - Group by model. - Calculate mean_absolute_residual as the mean of absolute_resid for each model.

67 anonymous responses

1. Other responses67

QUESTION 14 · Other responses · 1/10

Student responses

1. library(dplyr) model_errors %>% mutate(absolute_resid = abs(resid)) %>% group_by(model) %>% summarize(mean_absolute_residual = mean(absolute_resid))
2. model_errors library(dplyr) model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarise( mean_absolute_residual = mean(absolute_resid) ) |> arrange(mean_absolute_residual)
3. model_errors model_errors %>% mutate(absolute_resid = abs(resid)) %>% group_by(model) %>% summarize(mean_absolute_residual = mean(absolute_resid))
4. model_errors model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarize( mean_absolute_residual = mean(absolute_resid) )
5. model_errors model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarize(mean_absolute_residual = mean(absolute_resid)) |> arrange(mean_absolute_residual)
6. model_errors %>% mutate(absolute_resid = abs(resid)) %>% group_by(model) %>% summarize(mean_absolute_residual = mean(absolute_resid))
7. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by()

QUESTION 14 · Other responses · 2/10

Student responses

1. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::mutate(mean_absolute_residual = mean(abs_resid))
2. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::mutate(mean_absolute_residual = mean(abs_resid))
3. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid, na.rm = TRUE))
4. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid, na.rm = TRUE))
5. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid))
6. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid))
7. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid))

QUESTION 14 · Other responses · 3/10

Student responses

1. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid))
2. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid))
3. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid))
4. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid))
5. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid))
6. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid))
7. model_errors |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_abs_resid = mean(abs_resid))

QUESTION 14 · Other responses · 4/10

Student responses

1. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::mutate(mean_absolute_resid = mean(absolute_resid))
2. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::mutate(mean_absolute_resid = mean(absolute_resid))
3. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::mutate(mean_absolute_residual = mean(absolute_resid))
4. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::mutate(mean_absolute_residual = mean(absolute_resid))
5. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::mutate(mean_absolute_residual = mean(absolute_resid))
6. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize( mean_absolute_residual = mean(absolute_resid), .groups = "drop" ) |> dplyr::arrange(mean_absolute_residual)

QUESTION 14 · Other responses · 5/10

Student responses

1. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_resid = mean(absolute_resid))
2. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
3. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
4. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
5. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
6. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
7. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))

QUESTION 14 · Other responses · 6/10

Student responses

1. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
2. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
3. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
4. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
5. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
6. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
7. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))

QUESTION 14 · Other responses · 7/10

Student responses

1. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
2. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
3. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
4. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
5. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
6. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
7. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))

QUESTION 14 · Other responses · 8/10

Student responses

1. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
2. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
3. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
4. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_residual = mean(absolute_resid))
5. model_errors |> dplyr::mutate(absolute_resid=abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_absolute_resid = mean(absolute_resid))
6. model_errors |> dplyr::mutate(mean_absolute_residual = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarize(mean_mean_absolute_residual = mean(mean_absolute_residual, na.rm = TRUE))

QUESTION 14 · Other responses · 9/10

Student responses

1. model_errors |> mutate( absolute_resid = abs(resid) ) |> group_by(model) |> summarize( mean_absolute_residual = mean(absolute_resid) )
2. model_errors |> mutate( absolute_resid = abs(resid) ) |> group_by(model) |> summarize( mean_absolute_residual = mean(absolute_resid) )
3. model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarise( mean_absolute_residual = mean(absolute_resid) )
4. model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarise(mean_absolute_residual = mean(absolute_resid))
5. model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarize(mean_absolute_residual = mean(absolute_resid))
6. model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarize(mean_absolute_residual = mean(absolute_resid))
7. model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarize(mean_absolute_residual = mean(absolute_resid))

QUESTION 14 · Other responses · 10/10

Student responses

1. model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarize(mean_absolute_residual = mean(absolute_resid))
2. model_errors |> dplyr::mutate(absolute_resid = abs(resid)) |> dplyr::group_by(model) |> dplyr::summarise( mean_absolute_residual = mean(absolute_resid) )
3. model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarize(mean_absolute_residual = mean(absolute_resid))
4. model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarise(mean_absolute_residual = mean(absolute_resid))
5. model_errors |> mutate(absolute_resid = abs(resid)) |> group_by(model) |> summarize(mean_absolute_residual = mean(absolute_resid))
6. model_errors["absolute_resid"] = model_errors["resid"].abs() # 2 & 3. Group by model and calculate the mean of absolute_resid summary = ( model_errors.groupby("model")["absolute_resid"] .mean() .reset_index(name="mean_absolute_residual") ) print(summary)

CLASS RECORD · QUESTION 15

- Put the countries with the largest residuals first. - Keep the first five countries. - Display country, happiness, gdp_per_capita, and resid.

64 anonymous responses

1. Other responses64

QUESTION 15 · Other responses · 1/8

Student responses

1. happiness_residuals
2. happiness_residuals happiness_residuals %>% arrange(desc(resid)) %>% slice_head(n = 5) %>% select(country, happiness, gdp_per_capita, resid)
3. happiness_residuals happiness_residuals |> arrange(desc(resid)) |> slice_head(n = 5) |> select(country, happiness, gdp_per_capita, resid)
4. happiness_residuals library(dplyr) happiness_residuals |> arrange(desc(resid)) |> slice(1:5) |> select(country, happiness, gdp_per_capita, resid)
5. happiness_residuals %>% arrange(desc(resid)) %>% slice_head(n = 5) %>% select(country, happiness, gdp_per_capita, resid)
6. happiness_residuals %>% arrange(desc(resid)) %>% slice_head(n = 5) %>% select(country, happiness, gdp_per_capita, resid)
7. happiness_residuals |> arrange(desc(resid)) |> slice_head(n = 5) |> select(country, happiness, gdp_per_capita, resid)
8. happiness_residuals |> arrange(desc(resid)) |> slice_head(n = 5) |> select(country, happiness, gdp_per_capita, resid)
9. happiness_residuals |> arrange(desc(resid)) |> slice_head(n = 5) |> select(country, happiness, gdp_per_capita, resid)

QUESTION 15 · Other responses · 2/8

Student responses

1. happiness_residuals |> arrange(desc(resid)) |> slice_head(n = 5) |> select(country, happiness, gdp_per_capita, resid)
2. happiness_residuals |> arrange(desc(resid)) |> slice_head(n = 5) |> select(country, happiness, gdp_per_capita, resid)
3. happiness_residuals |> arrange(desc(resid)) |> slice(1:5) |> select(country, happiness, gdp_per_capita, resid)
4. happiness_residuals |> arrange(desc(resid)) |> slice(1:5) |> select(country, happiness, gdp_per_capita, resid)
5. happiness_residuals |> arrange(desc(resid)) |> slice(1:5) |> select(country, happiness, gdp_per_capita, resid)
6. happiness_residuals |> arrange(desc(resid)) |> slice(1:5) |> select(country, happiness, gdp_per_capita, resid)
7. happiness_residuals |> dplyr::arrange(desc(resid))
8. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::select(country, happiness, gdp_per_capita, resid) |> slice_head(n = 5)
9. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5)

QUESTION 15 · Other responses · 3/8

Student responses

1. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5)
2. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5)
3. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5)
4. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, gdp_year, life_years, rank, life_lower, life_upper, iso3, wb_country, income_thousands, income_doublings, resid)
5. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, gdp_year, resid)
6. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, gdp_year, resid)
7. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
8. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)

QUESTION 15 · Other responses · 4/8

Student responses

1. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
2. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
3. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
4. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
5. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
6. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
7. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
8. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)

QUESTION 15 · Other responses · 5/8

Student responses

1. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
2. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
3. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
4. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
5. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
6. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
7. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
8. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)

QUESTION 15 · Other responses · 6/8

Student responses

1. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
2. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
3. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
4. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
5. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
6. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
7. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
8. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)

QUESTION 15 · Other responses · 7/8

Student responses

1. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
2. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
3. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
4. happiness_residuals |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
5. happiness_residuals |> dplyr::arrange(desc(resid)) |> head(5) |> select(country, happiness, gdp_per_capita, resid)
6. happiness_residuals |> dplyr::arrange(desc(resid)) |> head(5) |> select(country, happiness, gdp_per_capita, resid)
7. happiness_residuals |> dplyr::arrange(desc(resid)) |> head(5)|> select(country, happiness, gdp_per_capita, resid)
8. happiness_residuals |> dplyr::arrange(dplyr::desc(resid)) |> dplyr::slice_head(n = 5) |> dplyr::select(country, happiness, gdp_per_capita, resid)

QUESTION 15 · Other responses · 8/8

Student responses

1. happiness_residuals |> dplyr::arrange(dplyr::desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
2. happiness_residuals |> dplyr::mutate(abs_resid = abs(resid)) |> dplyr::arrange(desc(resid)) |> dplyr::slice(1:5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
3. happiness_residuals |> dplyr::arrange(desc(resid)) |> head(n = 5) |> dplyr::select(country, happiness, gdp_per_capita, resid)
4. happiness_residuals |> arrange(desc(resid)) |> slice_head(n = 5) |> select(country, happiness, gdp_per_capita, resid)
5. happiness_residuals |> arrange(desc(resid)) |> slice_head(n=5) |> select(country, happiness, gdp_per_capita, resid)
6. top_five_above = happiness_residuals.sort_values(by="resid", ascending=False).head(5)[ ["country", "happiness", "gdp_per_capita", "resid"] ] print(top_five_above)

CLASS RECORD · QUESTION 16 · 1/2

Choose a country whose life rating surprised you. What additional data would you want to explain its position? Describe one analysis you would try.

70 anonymous responses

1. Named country plus demographic note14Student names Costa Rica and mentions population size as a relevant factor to investigate.
2. Individual income and happiness link10Student suggests analyzing individual-level earnings alongside personal happiness scores to explain life ratings.
3. Named country: China10Student names China as the surprising country with no additional details or analyses offered.
4. Named country: Singapore9Student simply names Singapore as the surprising country without further data or analysis suggestions.

CLASS RECORD · QUESTION 16 · 2/2

Response themes for question 16

1. Requests data on work, housing, health, poverty8Students ask for specific socioeconomic and health variables—hours worked, housing, healthcare access, life expectancy, poverty rates—to explain life ratings.
2. Suggests crime or healthcare data7Student proposes examining crime rates or measures of healthcare quality to explain a country's life rating.
3. Costa Rica with desire for experiments or demographics6Student names Costa Rica and requests controlled studies or mentions demographic/education/employment variables as relevant.
4. Unclear or no content6Responses that are empty, punctuation-only, or too vague to infer a viewpoint or suggested data/analysis.

QUESTION 16 · Named country plus demographic note · 1/2

Student responses

1. costa rica
2. costa rica and population size
3. Costa Rica surprised me. I think we would need something like climate to explain more of this data.
4. Costa Rica surprises me; I'd like to check cost of living to see if it anti-correlates
5. Costa Rica. I'd want to look at the cost of living and the cost of healthcare, because income isn't the only factor that can explain the ratings. I would construct another linear regression model.
6. I am surprised by Costa Rica, as it is a very poor country. I would be interested in looking at its quality of life or other similar stats.
7. I think Costa Rica, I can't imagine this, since it's located in Latin America
8. I want to look at the disparity between the wealthy and those who aren't, maybe looking at the median for income rather than average
9. i would look at the cost of living in each country and see if including that in the model would increase the accuracy. Also climate and location

QUESTION 16 · Named country plus demographic note · 2/2

Student responses

1. I would want to look at how peaceful that country is maybe. Not really shocked by any of them.
2. I'm surprised by how high costa rica is because it is such a small island, and I would think there would be a lower quality of life
3. Mozambique, I'd want to look at other characteristics included in the table like number of pets.
4. Other data like crime and housing rates, as well as cost of living would be important. Costa Rica was by far the biggest suprise out of the countres for me
5. Qualityb of living could be another data piece we could use, analyzing those residuals based on another factor

QUESTION 16 · Individual income and happiness link · 1/2

Student responses

1. Costa Rica Surprised me, I would want to see data about government spending/ public works, I would analyze approval of government spending
2. Costa Rica surprised me. I would run a more detailed linear relationship on jsut Costa Rica
3. Costa Rica surprised me. I would want to examine life expectancy, political freedom.
4. finland I wanna try it's population policy
5. I did not know that Costa Rica would have such a high life rating.
6. I think the US surprised me I would want to look into what its like for different incomes
7. I was surprised by Costa Rica. It may be interesting to examine climate data, and rank the countries by a set temperature or precipitation range.
8. I would look into the rights that citizens have in each country, to see if more individual rights/freedoms contributes to higher overall happiness
9. Maybe on an individual basis, the money you earn and your happiness score

QUESTION 16 · Individual income and happiness link · 2/2

Student responses

1. Singapore is quite surprising due to its higher GDP with lower happiness. We can do a multiregression test, adding more variables social support freedom health / life expectancy corruption perception work-life balance inequality

QUESTION 16 · Named country: China · 1/3

Student responses

1. china
2. Costa Rica surprised me because its life rating is 7.44 even though GDP per person is only about $27,000, well above what the line predicts. I would want data on social support, healthy life expectancy, and perceived freedom or corruption, which the World Happiness Report also measures. I would fit a multiple regression adding social support to income doublings and check whether Costa Rica's residual gets smaller and whether the model's R-squared improves.
3. Costa Rica surprised me because its life satisfaction is very high even though its GDP per person is much lower than many other highly ranked countries. I would look at factors like social support, health, life expectancy, and work-life balance, and compare those measures with countries that have similar GDP levels to see what might explain Costa Rica’s higher happiness.
4. finland. I would also like to see average days of sunlight

QUESTION 16 · Named country: China · 2/3

Student responses

1. I would investigate Costa Rica. I would collect data on social support, healthcare, and life expectancy. Then, I would use multiple linear regression to examine whether these factors help explain Costa Rica's higher-than-predicted happiness.
2. living cost, competition pressure
3. Mozambique, id want to know why it has the highest residual. maybe data on the level of healthcare or education and run a linear regression on it and the happiness
4. osta Rica surprised me: its life rating sits well above what its GDP per person predicts (a large positive residual), so income alone doesn’t explain why it’s so happy. Additional data I’d want: measures that capture quality of life beyond raw output. Social support and trust (do people have someone to count on), healthy life expectancy, freedom to make life choices, inequality (a Gini coefficient, since average GDP hides how income is spread), and generosity or corruption levels.
5. The U.S.'s position surprised me, so I would look at the difference between good lives and bad lives within America.

QUESTION 16 · Named country: China · 3/3

Student responses

1. United States supresied me the most beause it was right on the line and in the middle. Some more data that would be nie is other facotrs that play a role in deciding the happiness becasue our variable was already simply defined.

QUESTION 16 · Named country: Singapore · 1/2

Student responses

1. I was surprised by Costa Rica as you normally only hear about the Nordic countrys being very happy and not much in the west as much. I would want to see what the quality of life actually looks like to see how many hours people work a week, what their healthcare system is like, and the amount of income inequality they experience.
2. I would investigate categorical variables such as which factors are causing the most unhappiness like cost of living, healthcare, politics, etc. and then perform and independence test to see whether these variables are independent or not
3. other things like health, freedom of speech, etc
4. Singapore surprised me the most. I would look into cost of living and CPI in that country to investigate people's buying power, maybe marriage/divorce rates, and level of education. I would do this by plotting points on a boxplot to seperate effects.
5. Singapore, because I was expecting it to be a higher life rating
6. Singapore, probably wealth distribution , laws

QUESTION 16 · Named country: Singapore · 2/2

Student responses

1. Singapore. I really like its strict laws to protect overall safety, but maybe the area is too limited and the climate is too hot, and people too busy so the happiness level is not as high as I expected, I think we can add a working load analysis
2. Singapore. Maybe woking time per week. (High gdp could due to long working time, thus making happiness less). I will fit a new model using all variables
3. singnapore, it was lower thsan expected; one analysis would be anova

QUESTION 16 · Requests data on work, housing, health, poverty · 1/2

Student responses

1. Costa Rica surprised me because it has a high happiness score even though its GDP per person is much lower than many wealthy countries. I would want data on social support, healthcare, and work-life balance to understand why. I would try a regression analysis that includes these factors along with GDP per person to see whether they help explain Costa Rica's high happiness rating.
2. Costa Rica. Because its happiness rating is surprisingly high compared to its GDP per person. I would like to look at data on social support, healthcare, and work-life balance. I would try adding these factors to the regression model to see if they help explain why Costa Rica has such a high happiness rating.
3. Could investigate population statistics, employment, health, etc.
4. I think that Costa Rica was quite surprising. I think I'd want to use life quality factors like crime rate and social pressure to investigate them.
5. I think we need to see life expectancy as well as also poverty rates and any other quality of life factors
6. I would try and get data on hours worked per week for each country.

QUESTION 16 · Requests data on work, housing, health, poverty · 2/2

Student responses

1. it surprised me that kosovo had such a large residual, I would want to look at life expectancy or cost of living, and regress happiness on income, gdp, cost of living, and life expectancy to see if they explain more of the variance
2. other current events happening within the country

QUESTION 16 · Suggests crime or healthcare data · 1/1

Student responses

1. data on crime and safety in that country
2. I want to analyze the correlation between education level or itensity with happiness.
3. I would collect data on healthcare. While the GDP of a country is telling of its healthcare, it's not always the most accurate. In some cases, countries could be investing more into healthcare than expected of their GDP, so the people might be happier despite having less income because the health system will still take care of them.
4. I would look at crime rates or healthcare quality, using similar analysis like scatterplots and regression on a country with a high residual in our current analysis, like costa rica.
5. I would want to investigate data related to housing and healthcare.
6. Maybe try the meat demind or "Big mac" price score
7. zimbabwe

QUESTION 16 · Costa Rica with desire for experiments or demographics · 1/1

Student responses

1. Costa Rica, I would investigate healthcare and life expectancy amd make another linear model w more predictors
2. costa rica, i would want more controlled experiments man idk
3. Employment and Education Data, average amount of kids , healthcare, ertc. I am interested in costa rica. I would test on scatterplots and lms for associations between these and happiness.
4. I was mostly surprised with Costa Rica, as its economy I would say is more dependent on things such as tourism. I would thus want to filter out certain economic areas.
5. I'm surprised by how high Costa Rica's life rating is given its current GDP, I would want to learn more about its healthcare system specifically to help explain its position, I would try to quantify where in the world its healthcare system lies
6. Mozambique, they are generally happy despite having a really low gdp per capita. I would like to see if maybe money is not a huge thing in their culture and they barter rather than buy things. I would like to see the amount of transactions done with money.

QUESTION 16 · Unclear or no content · 1/1

Student responses

1. A country like Mozambique surprised me, since I expected countries affected by colonialism to have lower happiness scores. I am curious if we added the factor of colonized by European powers, what would our plot look like.
2. Afghanistan. I would survey threat to life, feeling of safety, living condition, you might have to separate based on gender
3. Costa Rica. I would also like to investigate its geographical position, i.e. it is located in Latin America. I will try to compare countries in different regions (Asia/Europe/Africa/Australia/North America/Latin America/South America).
4. Health
5. I think more layered data would be helpful, like some indirect factor could reveal more things
6. Political satisfaction could be a useful way to analyze life rating. If most people feel their government is doing a relatively good job/ they feel their taxes are supporting them then they could be happier even if they have a relatively low income.