Load Packages
library(fastDummies)
library(tidyverse)
library(psych)
Create a DataSet
# Create a vector of race scores
race <- c("White", "Black", "Asian", "Hispanic", "Other")
# Generate random income values for each race (100 cases)
set.seed(123) # for reproducibility
income <- round(runif(100, min = 20000, max = 100000), digits = 2)
# Repeat each race 20 times to get 100 cases
race <- rep(race, each = 20)
# Combine race and income into a data frame
data <- data.frame(race, income)
# Print the first few rows of the dataset
print(head(data))
## race income
## 1 White 43006.20
## 2 White 83064.41
## 3 White 52718.15
## 4 White 90641.39
## 5 White 95237.38
## 6 White 23644.52
Create Dummy Variables
data<-data %>% dummy_cols(select_columns = "race")
Regress Income on Race (African Americans as the Reference Category)
fit<-lm(income ~ race_Asian + race_Hispanic + race_Other + race_White, data=data)
summary(fit)
##
## Call:
## lm(formula = income ~ race_Asian + race_Hispanic + race_Other +
## race_White, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44169 -19531 -1137 18010 40481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 66138 5066 13.055 <2e-16 ***
## race_Asian -15015 7165 -2.096 0.0388 *
## race_Hispanic -7004 7165 -0.977 0.3308
## race_Other -7173 7165 -1.001 0.3193
## race_White -2073 7165 -0.289 0.7730
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22660 on 95 degrees of freedom
## Multiple R-squared: 0.05237, Adjusted R-squared: 0.01247
## F-statistic: 1.313 on 4 and 95 DF, p-value: 0.2709
