#Input Dataset
data=read.table(file.choose(),header = T)
data## Y X1 X2 X3
## 1 57.5 78 2.75 29.5
## 2 52.8 69 2.15 26.3
## 3 61.3 77 4.41 32.2
## 4 67.0 88 5.52 36.5
## 5 53.5 67 3.21 27.2
## 6 62.7 80 4.32 27.7
## 7 56.2 74 2.31 28.3
## 8 68.5 94 4.30 30.3
## 9 69.2 102 3.71 28.7#Analisis Model Regresi 1 (Y~X1)
library (lmtest)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary (car)## Loading required package: carDatalibrary (zoo)
m1=lm(Y~X1,data = data)
summary(m1)##
## Call:
## lm(formula = Y ~ X1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6440 -1.9128 -0.2151 2.2513 2.4075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.01108 5.42272 3.506 0.009915 **
## X1 0.51797 0.06635 7.807 0.000106 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.174 on 7 degrees of freedom
## Multiple R-squared: 0.897, Adjusted R-squared: 0.8823
## F-statistic: 60.95 on 1 and 7 DF, p-value: 0.0001065#Uji Asumsi ` #Uji Heteroskedastisitas #Uji Breusch Pagan
bptest(m1,varformula=~fitted.values(m1),studentize=FALSE) ##
## Breusch-Pagan test
##
## data: m1
## BP = 0.35178, df = 1, p-value = 0.5531#Uji Glejser
e1= resid (m1)
e1_abs= abs(e1)
glejeser_m1= lm (e1_abs~X1, data=data)
summary(glejeser_m1)##
## Call:
## lm(formula = e1_abs ~ X1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3200 -0.4062 0.2952 0.5324 0.7722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.57193 2.04979 -0.279 0.788
## X1 0.02864 0.02508 1.142 0.291
##
## Residual standard error: 0.8219 on 7 degrees of freedom
## Multiple R-squared: 0.157, Adjusted R-squared: 0.03657
## F-statistic: 1.304 on 1 and 7 DF, p-value: 0.2911#Uji non autokorelasi #uji Durbin-Watson
dwtest(m1,alternative="two.sided")##
## Durbin-Watson test
##
## data: m1
## DW = 1.785, p-value = 0.5744
## alternative hypothesis: true autocorrelation is not 0#uji Breusch-Godfrey
bgtest(m1)##
## Breusch-Godfrey test for serial correlation of order up to 1
##
## data: m1
## LM test = 0.033549, df = 1, p-value = 0.8547#Uji Normalitas Residual
shapiro.test(resid(m1))##
## Shapiro-Wilk normality test
##
## data: resid(m1)
## W = 0.87986, p-value = 0.1565#Analisis Model Regresi 2 (Y~X2)
library (lmtest)
library (car)
library (zoo)
m2=lm(Y~X2,data = data)
summary(m2)##
## Call:
## lm(formula = Y ~ X2, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6496 -2.1172 -1.2392 0.9339 7.8929
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.298 5.254 8.621 5.64e-05 ***
## X2 4.315 1.390 3.105 0.0172 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.394 on 7 degrees of freedom
## Multiple R-squared: 0.5793, Adjusted R-squared: 0.5192
## F-statistic: 9.64 on 1 and 7 DF, p-value: 0.0172#Uji Asumsi #Uji Heteroskedastisitas #Uji Breusch Pagan
bptest(m2,varformula=~fitted.values(m2),studentize=FALSE) ##
## Breusch-Pagan test
##
## data: m2
## BP = 0.061595, df = 1, p-value = 0.804#Uji Glejser
e2= resid (m2)
e2_abs= abs(e2)
glejeser_m2= lm (e2_abs~X2, data=data)
summary(glejeser_m2)##
## Call:
## lm(formula = e2_abs ~ X2, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3452 -1.7837 -0.6405 1.2837 4.7895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4690 3.1460 0.467 0.655
## X2 0.4406 0.8321 0.529 0.613
##
## Residual standard error: 2.631 on 7 degrees of freedom
## Multiple R-squared: 0.03851, Adjusted R-squared: -0.09885
## F-statistic: 0.2804 on 1 and 7 DF, p-value: 0.6128#Uji non autokorelasi #uji Durbin-Watson
dwtest(m2,alternative="two.sided")##
## Durbin-Watson test
##
## data: m2
## DW = 0.50193, p-value = 0.005426
## alternative hypothesis: true autocorrelation is not 0#uji Breusch-Godfrey
bgtest(m2)##
## Breusch-Godfrey test for serial correlation of order up to 1
##
## data: m2
## LM test = 5.4817, df = 1, p-value = 0.01922#Uji Normalitas Residual
shapiro.test(resid(m2)) ##
## Shapiro-Wilk normality test
##
## data: resid(m2)
## W = 0.93927, p-value = 0.5742#Analisis Model Regresi 3 (Y~X3)
library (lmtest)
m3=lm(Y~X3,data = data)
summary(m3)##
## Call:
## lm(formula = Y ~ X3, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.693 -3.315 -2.592 3.937 9.297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 27.1873 18.9650 1.434 0.195
## X3 1.1399 0.6369 1.790 0.117
##
## Residual standard error: 5.611 on 7 degrees of freedom
## Multiple R-squared: 0.314, Adjusted R-squared: 0.216
## F-statistic: 3.204 on 1 and 7 DF, p-value: 0.1166#Uji Asumsi
#Uji Heteroskedastisitas #Uji Breusch Pagan
bptest(m3,varformula=~fitted.values(m3),studentize=FALSE) ##
## Breusch-Pagan test
##
## data: m3
## BP = 0.32297, df = 1, p-value = 0.5698#Uji Glejser
e3= resid (m3)
e3_abs= abs(e3)
glejeser_m3= lm (e3_abs~X3, data=data)
summary(glejeser_m3)##
## Call:
## lm(formula = e3_abs ~ X3, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6039 -1.0955 -1.0751 -0.4915 4.5679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.4360 7.5924 1.77 0.120
## X3 -0.3034 0.2550 -1.19 0.273
##
## Residual standard error: 2.246 on 7 degrees of freedom
## Multiple R-squared: 0.1682, Adjusted R-squared: 0.0494
## F-statistic: 1.416 on 1 and 7 DF, p-value: 0.2729#Uji non autokorelasi #uji Durbin-Watson
dwtest(m3,alternative="two.sided")##
## Durbin-Watson test
##
## data: m3
## DW = 1.117, p-value = 0.1651
## alternative hypothesis: true autocorrelation is not 0#uji Breusch-Godfrey
bgtest(m3)##
## Breusch-Godfrey test for serial correlation of order up to 1
##
## data: m3
## LM test = 0.80317, df = 1, p-value = 0.3701#Uji Normalitas Residual
shapiro.test(resid(m3)) ##
## Shapiro-Wilk normality test
##
## data: resid(m3)
## W = 0.81761, p-value = 0.03238#Analisis Model Regresi 4 (Y~X1+X2+X3)
library(lmtest)
m4=lm(Y~X1+X2+X3,data = data)
summary(m4)##
## Call:
## lm(formula = Y ~ X1 + X2 + X3, data = data)
##
## Residuals:
## 1 2 3 4 5 6 7 8 9
## -0.2981 -0.2142 0.4714 -0.4746 -0.9524 0.4761 0.9274 0.7466 -0.6823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.87353 4.07389 5.369 0.00302 **
## X1 0.41277 0.03067 13.460 4.05e-05 ***
## X2 2.20267 0.47199 4.667 0.00550 **
## X3 -0.07895 0.15551 -0.508 0.63330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8484 on 5 degrees of freedom
## Multiple R-squared: 0.9888, Adjusted R-squared: 0.9821
## F-statistic: 147.1 on 3 and 5 DF, p-value: 2.696e-05#Uji Asumsi #Uji Multikolinearitas
vif(m4)## X1 X2 X3
## 1.403219 3.093549 2.607991#Uji Heteroskedastisitas #Uji Breusch Pagan
bptest(m4,varformula=~fitted.values(m4),studentize=FALSE) ##
## Breusch-Pagan test
##
## data: m4
## BP = 0.031878, df = 1, p-value = 0.8583#Uji Glejser
e4= resid (m4)
e4_abs= abs(e4)
glejeser_m4= lm (e4_abs~X1+X2+X3, data=data)
summary(glejeser_m4)##
## Call:
## lm(formula = e4_abs ~ X1 + X2 + X3, data = data)
##
## Residuals:
## 1 2 3 4 5 6 7 8
## -0.24968 -0.37078 -0.06495 -0.01987 0.35744 -0.17650 0.37477 0.12608
## 9
## 0.02347
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.000238 1.548904 0.646 0.547
## X1 0.002391 0.011660 0.205 0.846
## X2 0.035130 0.179452 0.196 0.853
## X3 -0.024936 0.059127 -0.422 0.691
##
## Residual standard error: 0.3226 on 5 degrees of freedom
## Multiple R-squared: 0.04467, Adjusted R-squared: -0.5285
## F-statistic: 0.07793 on 3 and 5 DF, p-value: 0.9692#Uji non autokorelasi #uji Durbin-Watson
dwtest(m4,alternative="two.sided")##
## Durbin-Watson test
##
## data: m4
## DW = 1.6446, p-value = 0.233
## alternative hypothesis: true autocorrelation is not 0#uji Breusch-Godfrey
bgtest(m4)##
## Breusch-Godfrey test for serial correlation of order up to 1
##
## data: m4
## LM test = 0.51249, df = 1, p-value = 0.4741#Uji Normalitas Residual
shapiro.test(resid(m4)) ##
## Shapiro-Wilk normality test
##
## data: resid(m4)
## W = 0.93208, p-value = 0.5014