setwd("C:/Users/Qiu J/Desktop/MSSP+DA 2021FALL/MSSP 897-002 Applied Linear Modeling/Assignment/Lab Assignment 4")
NLSY <- read.csv("C:/Users/Qiu J/Desktop/MSSP+DA 2021FALL/MSSP 897-002 Applied Linear Modeling/Assignment/Lab Assignment 4/NLSY-1.csv")
- The variable race has three levels: 1 = Hispanic 2 = Black 3 = Non-Black, Non-Hispanic Create two dummy variables using the race variable: called race_black (where children who identify as black are coded as 1 and all other children are coded as 0) and race_hispanic where children who identify as hispanic are coded as 1 and all other children are coded as 0).
NLSY$race_black <- ifelse(NLSY$race==2,1,0)
NLSY$race_hispanic <- ifelse(NLSY$race==1,1,0)
- Estimate a regression model where reading achievement scores (read) are regressed on race_black and race_hispanic.
lm <- lm(read1998 ~ race_black + race_hispanic, data=NLSY)
summary(lm)
Call:
lm(formula = read1998 ~ race_black + race_hispanic, data = NLSY)
Residuals:
Min 1Q Median 3Q Max
-42.625 -8.996 0.375 9.375 36.100
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 107.6254 0.3862 278.659 < 2e-16 ***
race_black -8.7255 0.6487 -13.451 < 2e-16 ***
race_hispanic -5.6290 0.7196 -7.823 7.39e-15 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 14.26 on 2662 degrees of freedom
Multiple R-squared: 0.06817, Adjusted R-squared: 0.06747
F-statistic: 97.38 on 2 and 2662 DF, p-value: < 2.2e-16
- Interpret the regression coefficients for race_black and race_hispanic.
The coefficient for race_black is -8.73, which means that children who identify as black are predicted to have reading achievement scores that are 8.73 points lower on average than other children, holding all other independent variables constant.
The coefficient for race_hispanic is -5.63, which means that children who identify as hispanic are predicted to have reading achievement scores that are 5.63 points lower on average than other children, holding all other independent variables constant.
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