> library(alr3)
> library(car)
> mydata<-read.table("clipboard",header=TRUE)
> attach(mydata)
> mydata.female<-mydata[which(mydata$sex=="-1"),]
> mydata.male<-mydata[which(mydata$sex=="1"),]
> lm.sol.female<-lm(mydata.female$DISTRESS~mydata.female$L.Q)
> lm.sol.male<-lm(mydata.male$DISTRESS~mydata.male$L.Q)
> summary(lm.sol.male)$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.67612830 0.69797738 26.757498 6.830549e-25
mydata.male$L.Q 0.02748604 0.01076621 2.552992 1.519673e-02
> summary(lm.sol.female)$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.922461343 0.84464952 28.3223525 3.136208e-24
mydata.female$L.Q 0.004005503 0.01188643 0.3369811 7.383314e-01
> mydata$female<-ifelse(mydata$sex=="-1",1,0)
> mydata$femlq<-mydata$female*mydata$L.Q
> mydata
subject sex L.Q SUMempa PERS FANTA EMPA DISTRESS female femlq
1 1 -1 -57.14286 75 15 25 15 20 1 -57.14286
2 2 1 -28.57143 83 24 27 16 16 0 0.00000
3 3 1 100.00000 95 27 24 23 21 0 0.00000
4 4 -1 100.00000 101 21 25 25 30 1 100.00000
5 5 1 85.71429 113 23 31 29 30 0 0.00000
……
> lm.sol<-lm(mydata$DISTRESS~mydata$female+mydata$L.Q+mydata$femlq)
> summary(lm.sol)$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.67612830 0.72832892 25.642437 2.340125e-36
mydata$female 5.24633304 1.08877603 4.818560 8.639765e-06
mydata$L.Q 0.02748604 0.01123438 2.446602 1.705139e-02
mydata$femlq -0.02348054 0.01599752 -1.467761 1.468480e-01
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