code 14.1

# code 12.1의 자료를 사용


visit<-c(350,460,350,430,350,380,430,470,450,490,340,300,440,450,300)
fee<-c(5.5,7.5,8,8,6.8,7.5,4.5,6.4,7,5,7.2,7.9,5.9,5,7)
ad<-c(3.3,3.3,3,4.5,3,4,3,3.7,3.5,4,3.5,3.2,4,3.5,2.7)
region<-c("A","A","A","A","A","B","B","B","B","B","C","C","C","C","C")
interaction<-fee*ad
da<-c(1,1,1,1,1,0,0,0,0,0,0,0,0,0,0)
db<-c(0,0,0,0,0,1,1,1,1,1,0,0,0,0,0)

sale<-data.frame(visit, fee, ad, interaction, region, da, db)



result.14.1<-lm(visit ~ fee + ad + interaction + da + db, data=sale)

plot(result.14.1)  #전반적인 진단 한번에

dffits(result.14.1)

##           1           2           3           4           5           6 
## -1.60561377  1.39317518 -0.07260193  0.63397095 -0.28872271 -1.09360694 
##           7           8           9          10          11          12 
##  0.79987672  0.31615661  0.51347773 -0.96779139 -0.21484963 -0.45201607 
##          13          14          15 
##  0.23446294  0.88532722 -0.19578192

plot(dffits(result.14.1))

cooks.distance(result.14.1)

##            1            2            3            4            5 
## 0.3124081578 0.2001249278 0.0009873976 0.0743981236 0.0151842168 
##            6            7            8            9           10 
## 0.1779849821 0.1167790135 0.0179237726 0.0452686755 0.1643002720 
##           11           12           13           14           15 
## 0.0084944718 0.0363719816 0.0102050083 0.1261611783 0.0071297379

plot(cooks.distance(result.14.1))

par(mfrow=c(2,3))   #화면을 2x3분할한다

dia<-dfbetas(result.14.1)




plot(dia[,1], main="intercept")
plot(dia[,2], main="fee")
plot(dia[,3], main="ad")
plot(dia[,4], main="interaction")
plot(dia[,5], main="da")
plot(dia[,6], main="db")

hist(sale$visit, breaks=c(seq(200,600, by=40)), freq=FALSE)
lines(density(sale$visit))

boxplot(sale$visit)
x11()
rstudent(result.14.1)

##           1           2           3           4           5           6 
## -2.09237106  2.55889446 -0.08645065  0.32157935 -0.48471213 -1.44197816 
##           7           8           9          10          11          12 
##  0.46703510  0.60415911  0.85818648 -0.74228820 -0.38890626 -0.65284409 
##          13          14          15 
##  0.28333382  1.14851316 -0.25343530

hatvalues(result.14.1)

##         1         2         3         4         5         6         7 
## 0.3706140 0.2286447 0.4135851 0.7953560 0.2618883 0.3651527 0.7457565 
##         8         9        10        11        12        13        14 
## 0.2149740 0.2636216 0.6296132 0.2338318 0.3240456 0.4064510 0.3727278 
##        15 
## 0.3737379

plot(hatvalues(result.14.1), rstudent(result.14.1), 
        main="Outlier and Leverage Diagnostics for visit")