R "linear regression analysis and statistical models" page120,4.3

Problem # P120 4.3 
RM (LS = List ()) 
A = read.xlsx ( "xiti_4.xlsx", Sheet =. 3) 
names (A) = C ( "the ord", "the Y", "K", "L" ) 
the attach (A) 
FM = LM (the Y ~ log (K) + log (L)) linear regression model # 
EI = RESID (FM) 
X-cbind = (. 1, as.matrix (A [,. 3:. 4])) 
t = ti (ei, X) # external student residuals 
plot (fitted (fm), t ) # residuals plotted in FIG.

　　

 

 Seen from the residual figure it out, heterogeneity of variance

a1 = boxcox(fm,lambda = seq(0,1,by=0.1))

　　

 

 It is seen from the image, preferably [lambda] 0, i.e., logarithmic transformation

# Logarithmic transformation 
lm.log LM = (log (the Y) ~ log (L) + log (K)) 
Coef (lm.log) 

Summary (lm.log) 
the detach (A)

　　

> summary(lm.log)

Call:
lm(formula = log(Y) ~ log(L) + log(K))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7251 -0.1764 -0.0059  0.1707  1.3035 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.38004    0.26873   1.414    0.166    
log(L)       0.05699    0.04471   1.275    0.211    
log(K)       0.93065    0.04131  22.526   <2e-16 ***
---
Signif. codes:  
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.441 on 35 degrees of freedom
Multiple R-squared:  0.944,	Adjusted R-squared:  0.9408 
F-statistic:   295 on 2 and 35 DF,  p-value: < 2.2e-16