这是一篇关于该模块的一些理论方面的一个更开放式的问题作业代写

 

Section 1

 

1.Your answers for this question. And interpretations, and so on. This could be for one of the more open-ended questions about some of the theoretical aspects of the module.

2a.

FIGURE 1: Histogram of district-level black turnout percentage

Here is a histogram with axis labels and a main title, and I can add some interpretation about what I see in Figure 1.

2b.

Here I have a box plot, where I have labelled the x and y axes, labelled the categories, and added a main title. I can then interpret what I see in Figure 2. Note that I can include the figure title as a caption (as in Figure 1) or in the plot main title (as in Figure 2).

3.TABLE 1: regression model of voting-age black population and black turnout

========================

Model 1

————————

(Intercept)    37.59 ***

(0.64)

CVAP            0.20 ***

(0.03)

————————

R^2             0.03

Adj. R^2        0.03

Num. obs.    1237

========================

*** p < 0.001; ** p < 0.01; * p < 0.05

I have pasted in the output from screenreg when reporting the results of my regression model. I have used Courier font to make sure that the output is formatted correctly. However, I could also add in my own well-formatted tables. I can then interpret the results in Table 1.

4.FIGURE 3: Plot of district-level voting-age black population and black turnout

I have my plot here. I have added axis labels and a main title. I have also added a regression line. I can then add my interpretation of what I see in the plot.

Appendix

# Preliminary steps

setwd(“~/Desktop/PUBL0055”)

library(texreg)

### SECTION 1

# Read in the data set

blackturnout <- read.csv(“data/blackturnout.csv”)

# Multiply by 100 to convert to percentage points

blackturnout$turnout <- blackturnout$turnout * 100

blackturnout$CVAP <- blackturnout$CVAP * 100

# 2a

# Histogram of black turnout

hist(blackturnout$turnout,

ylim=c(0,400),

xlab=”Black Turnout Percentage”,

main=”Histogram of black turnout”)

# 2b

# Boxplots of black turnout by whether there was a black candidate

boxplot(turnout ~ candidate, data = blackturnout,

names = c(“No Black Candidate”, “Black Candidate”),

ylab = “Voter turnout (black voters)”,

xlab = “One or more black candidates”,

main=”Black voter turnout and black candidate”)

# 3

# Regression model with black turnout as Y and percentage of voting-age black individuals as X

turnout_cvap_ols <- lm(turnout ~ CVAP, blackturnout)

screenreg(turnout_cvap_ols)

# 4

# Plot of voting-age black population and black turnout

plot(blackturnout$CVAP, blackturnout$turnout,

xlab = ” Percentage of voting-age black population”,

ylab = “Voter turnout (black voters)”,

main=”Voter turnout versus voting-age population”)

# Add in the regression line

abline(turnout_cvap_ols, col = “blue”)


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