legislators.Rmd

---
title: "R Notebook"
output: html_notebook
---

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```{r}
library(readstata13)
r13 <- read.dta13("washington_hw2.dta")
```
How many representatives are in the data set?
```{r}
nrow(r13)
```
What is the composition of the House in terms of sex and party (in percentages or proportions)?
```{r}
male<- subset(r13, female==0)
(nrow(male)/nrow(r13))*100
```
Female
```{r}
female<- subset(r13, female==1)
(nrow(female)/nrow(r13))*100
```
Democrats
```{r}
democrats<- subset(r13, party==1)
(nrow(democrats)/nrow(r13))*100
```
Republicans
```{r}
republicans<- subset(r13, party==2)
(nrow(republicans)/nrow(r13))*100
```
independents
```{r}
independents<- subset(r13, party==3)
(nrow(independents)/nrow(r13))*100
```
Summary of aauw
```{r}
summary(r13$aauw)
```
What percentage of representatives have a score of 100?
```{r}
score100<- subset(r13, aauw==100)
(nrow(score100)/nrow(r13))*100
```
What percentage of representatives have a score of 0?
```{r}
score0<- subset(r13, aauw==0)
(nrow(score0)/nrow(r13))*100
```
 Run a regression
```{r}
model = lm(formula = aauw ~female, data = r13)
summary(model)
```
 Provide a correct interpretation of the estimated coefficient
A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase. A negative coefficient suggests that as the independent variable increases, the dependent variable tends to decrease.So in our case we can conclude that there is a positive correlation between the independent and the dependent variables.

Provide a correct interpretation of the estimated intercept. Is the intercept a substantively
meaningful quantity in this context?
The intercept is meaningful in this context.

Calculate the predicted values of the AAUW’s legislative score for male legislators and for
female legislators.
```{r}
testpredict = predict(model, newdata = r13, type = 'response')
summary(testpredict)
```
Compare these predicted values from (c) with the average AAUW legislative score for male
legislators and the average AAUW legislative score for female legislators (you can calculate
these conditional averages using mean()).
```{r}
male.mean<-mean(male$aauw)
female.mean<-mean(female$aauw)
```
First, create a new binary variable called republican that is coded =1 if the legislator is a Republican and is coded=0 if the legislator is a Democrat or Independent
```{r}
z <- c(r13$party)
r13$republican<-ifelse(r13$party==2,1,0)
```
Run a regression
```{r}
model1 = lm(formula = aauw ~female+republican, data = r13)
summary(model1)

testpredict4 = predict(model1, newdata = r13, type = 'response')
summary(testpredict4)
```
4
```{r}
r13$female.republican<-ifelse(r13$female==1&r13$party==1,1,0)
model2 = lm(formula = aauw ~female+republican+female.republican, data = r13)
summary(model2)

testpredict2 = predict(model2, newdata = r13, type = 'response')
summary(testpredict2)

```
5
```{r}
model3 = lm(formula = aauw ~demvote, data = r13)
summary(model3)

```
Scatter Plot
```{r}
plot( r13$demvote,r13$aauw, main = "Main title",
     xlab = "X axis title", ylab = "Y axis title",
     pch = 19, frame = FALSE)
# Add regression line
plot( r13$demvote,r13$aauw, main = "Main title",
     xlab = "X axis title", ylab = "Y axis title",
     pch = 19, frame = FALSE)
abline(lm(aauw ~ demvote, data = r13), col = "blue")
```
e
```{r}
testpredict3 = predict(model3, newdata = r13, type = 'response')
summary(testpredict3)
```


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