Statistics324_HW7 - xyplot(age~sqrt(igf1),data=

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Statistics 324 – Discussion 311 w/ Jack Homework 7 – Victoria Yakovleva 1. xyplot(body.weight~metabolic.rate,data = rmr,type=c("g","p","smooth")) According to this graph, a simple linear regression model doesn’t seem reasonable. xyplot(body.weight~metabolic.rate,data = rmr,type=c("g","p","r"))
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flm<-lm(body.weight~metabolic.rate,data=rmr) xyplot(resid(flm)~fitted(flm), type=c("g","p","smooth")) 2. older <- subset(juul, age > 25) fm1<-lm(age~sqrt(igf1),data=older)
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Unformatted text preview: xyplot(age~sqrt(igf1),data= older,type=c(&quot;g&quot;,&quot;p&quot;,&quot;smooth&quot;)) xyplot(age~sqrt(igf1),data=older,type=c(&quot;g&quot;,&quot;p&quot;,&quot;r&quot;)) xyplot(resid(fm1)~fitted(fm1), type=c(&quot;g&quot;,&quot;p&quot;,&quot;smooth&quot;)) I think this plot does indicate increasing variation with increasing level of response because there is more vertical variation on the right hand side of the plot than the left hand side. Its not perfect, but increasing variation is there....
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This note was uploaded on 03/27/2008 for the course STAT 324 taught by Professor Bates during the Fall '06 term at Wisconsin.

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Statistics324_HW7 - xyplot(age~sqrt(igf1),data=

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