Our data shows different demographic variable for counties including which party they voted for. Our task is to determine the impact of income on red voting states.Here is the summary of our model in which we will need the coefficients

election <- read.csv("https://www.macalester.edu/~ajohns24/data/IMAdata1.csv")
election$Red <- as.numeric(election$StateColor == "red")
redmod<-glm(Red ~ IncomeBracket, family = binomial, data = election)
summary(redmod)
## 
## Call:
## glm(formula = Red ~ IncomeBracket, family = binomial, data = election)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.2294  -1.2294  -0.9821   1.1263   1.3861  
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)      -0.47838    0.06356  -7.527 5.19e-14 ***
## IncomeBracketlow  0.59977    0.07717   7.772 7.74e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 4352.6  on 3142  degrees of freedom
## Residual deviance: 4291.1  on 3141  degrees of freedom
## AIC: 4295.1
## 
## Number of Fisher Scoring iterations: 4

Below is the probability of a high income area voting red.

A<-exp(coef(redmod)[1])
probhigh<-A/(A+1)
probhigh
## (Intercept) 
##   0.3826336

Below is the probability of a low income area voting red.

B<-exp((coef(redmod)[1]+coef(redmod)[2]))
problow<- B/(B+1)
problow
## (Intercept) 
##   0.5303103

The odds of a high income state being red are times the odds of a low income state being red.

probhigh/problow
## (Intercept) 
##   0.7215278