Using the 2012 Smoking and Drug Use Amongst English Pupils Dataset (2012smokedrugs.dta), perform diagnostics on the second cigarette consumption multiple linear regression model. To remind you, the outcome variable was cigs7, but recoded to remove all 0s, and the predictor variables were free, schyear, and sex.
Heteroscedasticity Diagnostics:
(a) Create a plot to test for heteroscedasticity.
(b) Perform a Breusch-Pagan test.
(c) If heteroscedasticity is present re-run the regression using robust standard errors. Are any predictors
that were statistically significant now not significant?
a.
x11()
plot(y=model.1$residuals,x=model.1$fitted.values, xlab="Fitted Values", ylab="Residuals")
abline(h=0, col="red")
There is somewhat of a fan pattern, possibly indicating heteroscedasticity.
b.
bptest(model.1, studentize=FALSE)
Breusch-Pagan test
data: model.1
BP = 1.1206, df = 3, p-value = 0.7721
c. Since we did not have heteroscedasticity, we do not need to attempt any corrections.
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