The dataset come from a small random sample of the U.S. National Longitudinal Survey of Youth.

`data(NLSY)`

A data frame with 243 observations on the following 6 variables.

`math`

Math achievement test score

`read`

Reading achievement test score

`antisoc`

score on a measure of child's antisocial behavior,

`0:6`

`hyperact`

score on a measure of child's hyperactive behavior,

`0:5`

`income`

yearly income of child's father

`educ`

years of education of child's father

In this dataset, `math`

and `read`

scores are taken at the outcome
variables.
Among the remaining predictors, `income`

and `educ`

might be
considered as background variables necessary to control for.
Interest might then be focused on whether
the behavioural variables `antisoc`

and `hyperact`

contribute beyond that.

# NOT RUN { data(NLSY) #examine the data scatterplotMatrix(NLSY, smooth=FALSE) # test control variables by themselves # ------------------------------------- mod1 <- lm(cbind(read,math) ~ income+educ, data=NLSY) Anova(mod1) heplot(mod1, fill=TRUE) # test of overall regression coefs <- rownames(coef(mod1))[-1] linearHypothesis(mod1, coefs) heplot(mod1, fill=TRUE, hypotheses=list("Overall"=coefs)) # additional contribution of antisoc + hyperact over income + educ # ---------------------------------------------------------------- mod2 <- lm(cbind(read,math) ~ antisoc + hyperact + income + educ, data=NLSY) Anova(mod2) coefs <- rownames(coef(mod2))[-1] heplot(mod2, fill=TRUE, hypotheses=list("Overall"=coefs, "mod2|mod1"=coefs[1:2])) linearHypothesis(mod2, coefs[1:2]) heplot(mod2, fill=TRUE, hypotheses=list("mod2|mod1"=coefs[1:2])) # }