# Pemodelan Regresi Linier Berganda dan Aplikasinya pada Data Saham #
setwd("c:/RMFR/latihan")
saham <- read.table("saham.txt",header=TRUE)
price <- lm(price~pe+eps+roi+roe+bv, data=saham)
summary(price)
##
## Call:
## lm(formula = price ~ pe + eps + roi + roe + bv, data = saham)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9511.7 -1452.0 245.7 1152.9 7525.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2875.1411 1309.0799 -2.196 0.0372 *
## pe -9.1001 11.9562 -0.761 0.4534
## eps -3.8971 7.5545 -0.516 0.6103
## roi 124.3549 214.9633 0.578 0.5679
## roe 70.6034 207.2762 0.341 0.7361
## bv 3.8976 0.2566 15.188 1.93e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3353 on 26 degrees of freedom
## Multiple R-squared: 0.9252, Adjusted R-squared: 0.9108
## F-statistic: 64.32 on 5 and 26 DF, p-value: 8.336e-14
# Uji asumsi multikolinieritas
library(car)
vif(price)
## pe eps roi roe bv
## 1.096377 5.776259 36.081309 44.468854 1.106299
# Uji asumsi heteroskedastisitas
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
bptest(price, studentize=FALSE, data=saham)
##
## Breusch-Pagan test
##
## data: price
## BP = 43.178, df = 5, p-value = 3.401e-08
# Uji asumsi autokorelasi
library(lmtest)
dwtest(price)
##
## Durbin-Watson test
##
## data: price
## DW = 2.2541, p-value = 0.7679
## alternative hypothesis: true autocorrelation is greater than 0
bgtest(price, order=6)
##
## Breusch-Godfrey test for serial correlation of order up to 6
##
## data: price
## LM test = 5.9147, df = 6, p-value = 0.4328
# diagnosa kenormalan error dengan grafik
par(mfrow=c(2,2))
plot(price,which=c(1:4))
# uji kenormalan error
galat <- resid(price)
shapiro.test(galat) # Uji Shapiro-Wilk
##
## Shapiro-Wilk normality test
##
## data: galat
## W = 0.92245, p-value = 0.02424
library(tseries)
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
jarque.bera.test(galat) # Uji Jarque Bera
##
## Jarque Bera Test
##
## data: galat
## X-squared = 8.3094, df = 2, p-value = 0.01569
library(nortest)
ad.test(galat) # Uji Anderson-Darling
##
## Anderson-Darling normality test
##
## data: galat
## A = 0.926, p-value = 0.01638
lillie.test(galat) # Uji Lilliefors/Kolmogorov-Smirnov
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: galat
## D = 0.14364, p-value = 0.09146