@@ -6,7 +6,7 @@ date-format: "[Last modified on] MMMM DD, YYYY HH:mm:ss zzz"
66format :
77 html : default
88 pdf : default
9- editor : visual
9+ editor : source
1010---
1111
1212``` {r include = FALSE}
@@ -89,7 +89,7 @@ Create a visual model of these data (a graph) that will allow you to conduct an
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9191::: {.callout-important icon="false" collapse="false" title="Problem 2 Answers"}
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@@ -108,15 +108,15 @@ What does the intercept mean in this regression table?
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114114::: {.callout-note icon="false" title="Problem 5"}
115115What does the model estimate as the number of hate crimes per 100,000 people in states with "low" Trump support?
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120120
121121``` {r}
122122# Type your code and comments inside the code chunk
@@ -134,7 +134,7 @@ Does the model estimate that hate crimes are more frequent in states that show "
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135135```
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@@ -147,7 +147,7 @@ How much greater were hate crimes in "medium" Trump-support states compared to "
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148148```
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@@ -160,25 +160,25 @@ What are the three possible fitted values $\hat{y}$ for this model? Hint: use th
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161161```
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171171Below we calculate the group means of hate crimes for the ` high ` , ` medium ` and ` low ` levels of Trump support. How do these numbers compare to the three possible fitted values $\widehat{y}$ for this model?
172172
173173``` {r}
174- hate_crimes %>%
175- group_by(trump_support) %>%
174+ hate_crimes |>
175+ group_by(trump_support) |>
176176 summarize(mean_hate_crimes = mean(hate_crimes, na.rm = TRUE))
177177```
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179179
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182182:::
183183
184184## The Regression Equation
@@ -189,7 +189,6 @@ The regression equation for this model is the following (render the document and
189189hate_mod <- lm(hate_crimes ~ trump_support, data = hate_crimes)
190190```
191191
192-
193192$$
194193\widehat{\text{hate\_crimes}} = `r coef(hate_mod)[1]` + `r coef(hate_mod)[2]` \times 1_{\text{low support}}(x) + `r coef(hate_mod)[3]` \times 1_{\text{med support}}(x)
195194$$
@@ -233,14 +232,14 @@ Solve the regression equation for a state in which `trump_support` is "high".
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235234::: {.callout-important icon="false" collapse="false" title="Problem 10 Answers"}
236- Your $LaTeX$ here
235+ Your $LaTeX$ here
237236
238237``` {r}
239238# Type your code and comments inside the code chunk
240239
241240```
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246245::: {.callout-note icon="false" title="Problem 11"}
@@ -255,7 +254,7 @@ Do these results surprise you? (There is no right answer to this question.)
255254
256255```
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259258:::
260259
261260# Hate Crimes and Unemployment
@@ -292,7 +291,6 @@ Write out the regression equation for `job_mod`.
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295-
296294Write your equation with $LaTeX$ here.
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@@ -301,12 +299,11 @@ Interpret the estimate of the intercept from the table below.
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308306What does the model estimate the number of hate crimes per 100,000 people to be in a state with "low" unemployment?
309-
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312309::: {.callout-important icon="false" collapse="false" title="Problem 16 Answers"}
@@ -315,7 +312,7 @@ What does the model estimate the number of hate crimes per 100,000 people to be
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316313```
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321318::: {.callout-note icon="false" title="Problem 17"}
@@ -328,7 +325,7 @@ What are the two possible fitted values $\widehat{y}$ for this model? Why are th
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329326```
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@@ -341,7 +338,7 @@ Use the `get_regression_points()` function to generate a table showing the fitte
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342339```
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345342:::
346343
347344# Hate Crimes and Household Income
@@ -362,7 +359,7 @@ Were there more hate crimes in areas with high or low median household incomes?
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@@ -386,7 +383,7 @@ Take a look at data for Maine (row 2 of `hate_crimes`). Did the model (`job_med_
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387384```
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391388
392389# ** EXTRA**
@@ -398,7 +395,7 @@ Take a look at data for Maine (row 2 of `hate_crimes`). Did the model (`job_med_
398395#| fig-cap: "Hate crimes in relation to Trump support"
399396# Reordering trump_support
400397library(forcats)
401- hate_crimes <- hate_crimes %>%
398+ hate_crimes <- hate_crimes |>
402399 mutate(trump_support = fct_relevel(trump_support,
403400 "low", "medium", "high"))
404401ggplot(data = hate_crimes, aes(x = trump_support, y = hate_crimes)) +
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