Chi-Square in R on by State Politics (Red/Blue) and Income (Higher/Lower)
This is a significant result, but instead of a logistic regression looking at the income average per state and the likelihood of being a Democratic state, it uses Chi-Square. Interpreting this is pretty straightforward, in that liberal states typically have cities and people that earn more money. When using adjusted incomes, by cost of living, this difference disappears.
# R - Chi Square rm(list = ls()) stateData <- read.table("CostByStateAndSalary.csv", header = TRUE, sep = ",") # Create vectors affluence.median <- median(stateData$Y2014, na.rm = TRUE) affluence.v <- ifelse(stateData$Y2014 > affluence.median, 1, 0) liberal.v <- stateData$Liberal # Solve pol.Data = table(liberal.v, affluence.v) result <- chisq.test(pol.Data) print(result) print(pol.Data)
Pearson's Chi-squared test with Yates' continuity correction data: pol.Data X-squared = 12.672, df = 1, p-value = 0.0003711 > print(pol.Data) + affluence.v liberal.v 0 1 0 22 7 1 4 16