The eigth assignment of Linear Regression. The assignment is written in Rmarkdown, a smart syntax supported by RStudio helping with formula, plot visualization and plugin codes running.

most recommend: click here for html version of assignment, you can see codes as well as plots.

You may also find the PDF Version of this assignment from github. Or if you can cross the fire wall, just see below:

1

a

# read the data
setwd('~/Desktop/三春/5线性回归分析/作业/HW8/')
dat<-read.csv("hw8.csv")
X1<-dat$x1
X2<-dat$x2
X3<-dat$x3
X4<-dat$x4
Y<-dat$y
# plot stem and leaf plots
stem(X1)
stem(X2)
stem(X3)
stem(X4)

It seems that X3 has a denser concentration and the following boxplot supports it. X1 has two outliers. X2 is asymmetric

library(vioplot)
vioplot(X1,X2,X3,X4,col="gold")
boxplot(X1,X2,X3,X4)

b

pairs(~X1+X2+X3+X4+Y,data=dat, 
   main="Scatterplot Matrix")
cor(dat)

obviously X3 and X4 has high correlation.

c

Fit = lm(Y~X1+X2+X3+X4, data=dat)
anova(Fit)
summary(Fit)

$\hat Y = -124.38 + 0.30 x_1 + 0.05 x_2 + 1.31 x_3 + 0.52 x_4$ It seems X2 should be excluded from the model since the p-value=0.4038.

2

a

library(leaps)
best <- function(model, ...) 
{
  subsets <- regsubsets(formula(model), model.frame(model), ...)
  subsets <- with(summary(subsets),
                  cbind(p = as.numeric(rownames(which)), which, adjr2))

  return(subsets)
}  
round(best(Fit, nbest = 6), 4)

The four best subset regression models are

b

There are $C_p$ Criterion, #AIC_p# and #SBC_p# which can be used as criterion to select the best model. They all place penalties for adding predictors.

3

library(MASS)
Null = lm(Y ~ 1, dat)

addterm(Null, scope = Fit, test="F")
NewMod = update( Null, .~. + X3)
addterm( NewMod, scope = Fit, test="F" )
NewMod = update( NewMod, .~. + X1)
dropterm(NewMod , test = "F")
addterm( NewMod, scope = Fit, test="F" )
NewMod = update( NewMod, .~. + X4)
dropterm( NewMod, test = "F" )
addterm( NewMod, scope = Fit, test="F" )

• As shown, start with no predictors, X3 is chosen because of smallest p-value.
• Then regressing y on x3 and additional one predictor, the result shows that X1 has the smallest p-value (1.578e-06< 0.05). Therefore X1 can be included in the model. In the same time a test is given to see if x3 should be dtropped. Since p-value (6.313e-13<0.10), X3 is retained.
• Then regressing y on X3, X1 and any one of the rest two, it shows that X4 has the smallest p-value (0.0007354 < 0.05) and hence being included in the model.In the same time a test is given to see if x3 or x1 should be dtropped. Since both of their p-value < 0.10, they are both retained.
• Finally, regressing y on all four predictors and x2 isn’t significant to be included (0.4038 > 0.05). Thus it is deleted from the model.
• The best subset of predictor variables to predict job proficiency is (x1,x3,x4)

b

The model evaluated using the forward stepwise regression shows the same result as earlier chosen variables under the criteria of adjusted R square.