library(modelsummary)
library(tidyverse)
library(sjPlot)
wage=read_csv("E:/hw/wage.csv")
summary(wage)
## wage educ exper nonwhite
## Min. : 5.53 Min. : 4.00 Min. : 1.00 Length:100
## 1st Qu.: 8.60 1st Qu.:12.00 1st Qu.: 5.00 Class :character
## Median :10.81 Median :12.00 Median :13.00 Mode :character
## Mean :11.85 Mean :12.82 Mean :15.03
## 3rd Qu.:13.64 3rd Qu.:14.00 3rd Qu.:22.00
## Max. :27.20 Max. :18.00 Max. :45.00
## female married
## Length:100 Length:100
## Class :character Class :character
## Mode :character Mode :character
##
##
##
#datasummary_skim(wage)
#draw a historgram graph for the variabe wage
ggplot(wage, aes(x = wage)) +
geom_histogram(binwidth = 2, fill = "blue", color = "black", alpha = 0.7) + # Change bindwith and alpha values to see what happens
labs(title = "Histogram of wage",
x = "wage", y = "Count") + # Add labels and title
theme_minimal() # Use a minimal theme for a clean look
#draw a scatter plot of wage against educ
plot(x = wage$educ, y = wage$wage, xlab = "years of education", ylab = "hourly wage", pch = 16, cex=0.3, xlim=c(3,20))
# Add text annotations
text(x = wage$educ, y = wage$wage, pos = 4, cex = 0.4)
# Fit a simple linear regression and add the regression line
m1 <- lm(wage ~ educ, data=wage)
abline(a=coef(m1)[1], b=coef(m1)[2], col="red")
#run OLS of wage on educ
model1<- lm(wage~ educ, data = wage)
wage$female <- car::recode(wage$female, "'Y'='female'; 'N'='male'", as.factor=TRUE)
wage$nonwhite <- car::recode(wage$nonwhite, "'Y'='nonwhite'; 'N'='white'", as.factor=TRUE)
wage$married <- car::recode(wage$married, "'Y'='married'; 'N'='unmarried'", as.factor=TRUE)
#run OLS of wage on educ and female
model2<-lm(wage~ educ+female, data = wage)
#run OLS of wage on educ, female and the interaction term between them
model3<-lm(wage~ educ+female+educ:female, data = wage)
#run OLS adding nonwhite and married
model4<-lm(wage~ educ+female+educ:female+nonwhite+married, data = wage)
#Create regression tables for the model using sjPlot packages
tab_model(model1, model2, model3, model4)
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wage
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wage
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wage
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wage
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Predictors
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Estimates
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CI
|
p
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Estimates
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CI
|
p
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Estimates
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CI
|
p
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Estimates
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CI
|
p
|
|
(Intercept)
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3.19
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-1.18 – 7.56
|
0.151
|
2.57
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-1.55 – 6.69
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0.219
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0.25
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-6.86 – 7.36
|
0.945
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1.20
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-6.17 – 8.57
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0.747
|
|
educ
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0.68
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0.34 – 1.01
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<0.001
|
0.62
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0.30 – 0.93
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<0.001
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0.80
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0.24 – 1.36
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0.005
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0.86
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0.31 – 1.42
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0.003
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female [male]
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|
|
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2.83
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1.32 – 4.35
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<0.001
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6.30
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-2.48 – 15.09
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0.158
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6.74
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-1.97 – 15.46
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0.128
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|
educ * female [male]
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|
|
|
|
|
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-0.27
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-0.95 – 0.41
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0.428
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-0.33
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-1.01 – 0.34
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0.331
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|
nonwhite [white]
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|
|
|
|
|
|
|
|
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-1.02
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-3.43 – 1.39
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0.404
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married [unmarried]
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|
|
|
|
|
|
|
|
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-1.43
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-3.00 – 0.15
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0.075
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|
Observations
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100
|
100
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100
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100
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R2 / R2 adjusted
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0.140 / 0.132
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0.247 / 0.232
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0.252 / 0.229
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0.285 / 0.247
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