#Memanggil Data
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
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##Regresi Linear Sederhana Model 1 (Y dan X1)
regm1=lm(Y~X1,data=data)
##UJI ASUMSI: Normalitas
#Uji Shapiro wilk
shapiro.test(resid(regm1))
##
## Shapiro-Wilk normality test
##
## data: resid(regm1)
## W = 0.87986, p-value = 0.1565
##UJI ASUMSI: Non-heteroskedastisitas
#Uji Glejser
e1=resid(regm1) #mencari residual
e1
## 1 2 3 4 5 6 7
## -1.9127561 -1.9510242 2.4052142 2.4075419 -0.2150838 2.2513035 -1.1408752
## 8 9
## 0.7997207 -2.6440410
ae1=abs(e1) #absolute residual
ae1
## 1 2 3 4 5 6 7 8
## 1.9127561 1.9510242 2.4052142 2.4075419 0.2150838 2.2513035 1.1408752 0.7997207
## 9
## 2.6440410
regae1=lm(ae1~X1,data=data)
summary(regae1)
##
## Call:
## lm(formula = ae1 ~ 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 Breush-Pagan
bptest(regm1)
##
## studentized Breusch-Pagan test
##
## data: regm1
## BP = 1.715, df = 1, p-value = 0.1903
##UJI ASUMSI: Non-autokorelasi
#Uji Durbin Watson
dwtest(regm1)
##
## Durbin-Watson test
##
## data: regm1
## DW = 1.785, p-value = 0.2872
## alternative hypothesis: true autocorrelation is greater than 0
#Uji Breush-Godfrey
bgtest(regm1)
##
## Breusch-Godfrey test for serial correlation of order up to 1
##
## data: regm1
## LM test = 0.033549, df = 1, p-value = 0.8547
##Regresi Linear Sederhana Model 1
summary(regm1)
##
## 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
library(lmtest)
##Regresi Linear Sederhana Model 2 (Y dan X2)
regm2=lm(Y~X2,data=data)
##UJI ASUMSI: Normalitas
#Uji Shapiro wilk
shapiro.test(resid(regm2))
##
## Shapiro-Wilk normality test
##
## data: resid(regm2)
## W = 0.93927, p-value = 0.5742
##UJI ASUMSI: Non-heteroskedastisitas
#Uji Glejser
e2=resid(regm2) #mencari residual
e2
## 1 2 3 4 5 6 7
## 0.3353100 -1.7757020 -3.0275565 -2.1171842 -5.6495807 -1.2392084 0.9339012
## 8 9
## 4.6470912 7.8929294
ae2=abs(e2) #absolute residual
ae2
## 1 2 3 4 5 6 7 8
## 0.3353100 1.7757020 3.0275565 2.1171842 5.6495807 1.2392084 0.9339012 4.6470912
## 9
## 7.8929294
regae2=lm(ae2~X2,data=data)
summary(regae2)
##
## Call:
## lm(formula = ae2 ~ 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 Breush-Pagan
bptest(regm2)
##
## studentized Breusch-Pagan test
##
## data: regm2
## BP = 0.072359, df = 1, p-value = 0.7879
##UJI ASUMSI: Non-autokorelasi
#Uji Durbin Watson
dwtest(regm2)
##
## Durbin-Watson test
##
## data: regm2
## DW = 0.50193, p-value = 0.002713
## alternative hypothesis: true autocorrelation is greater than 0
#Uji Breush-Godfrey
bgtest(regm2)
##
## Breusch-Godfrey test for serial correlation of order up to 1
##
## data: regm2
## LM test = 5.4817, df = 1, p-value = 0.01922
##Regresi Linear Sederhana Model 2
summary(regm2)
##
## 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
library(lmtest)
##Regresi Linear Sederhana Model 3 (Y dan X3)
regm3=lm(Y~X3,data=data)
##UJI ASUMSI: Normalitas
#Uji Shapiro wilk
shapiro.test(resid(regm3))
##
## Shapiro-Wilk normality test
##
## data: resid(regm3)
## W = 0.81761, p-value = 0.03238
##UJI ASUMSI: Non-heteroskedastisitas
#Uji Glejser
e3=resid(regm3) #mencari residual
e3
## 1 2 3 4 5 6 7 8
## -3.314678 -4.366959 -2.592442 -1.794065 -4.692880 3.937164 -3.246783 6.773392
## 9
## 9.297252
ae3=abs(e3) #absolute residual
ae3
## 1 2 3 4 5 6 7 8
## 3.314678 4.366959 2.592442 1.794065 4.692880 3.937164 3.246783 6.773392
## 9
## 9.297252
regae3=lm(ae3~X3,data=data)
summary(regae3)
##
## Call:
## lm(formula = ae3 ~ 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 Breush-Pagan
bptest(regm3)
##
## studentized Breusch-Pagan test
##
## data: regm3
## BP = 0.62733, df = 1, p-value = 0.4283
##UJI ASUMSI: Non-autokorelasi
#Uji Durbin Watson
dwtest(regm3)
##
## Durbin-Watson test
##
## data: regm3
## DW = 1.117, p-value = 0.08255
## alternative hypothesis: true autocorrelation is greater than 0
#Uji Breush-Godfrey
bgtest(regm3)
##
## Breusch-Godfrey test for serial correlation of order up to 1
##
## data: regm3
## LM test = 0.80317, df = 1, p-value = 0.3701
##Hasil Regresi Linear Sederhana Model 3
summary(regm3)
##
## 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
library(lmtest)
library(car)
## Loading required package: carData
##Regresi Linear Berganda Model 4 (Y dengan X1, X2, X3)
regrlb=lm(Y~X1+X2+X3,data=data)
##Pendeteksi Multikolinearitas
vif(regrlb)
## X1 X2 X3
## 1.403219 3.093549 2.607991
##UJI ASUMSI: Normalitas
#Uji Shapiro wilk
shapiro.test(resid(regrlb))
##
## Shapiro-Wilk normality test
##
## data: resid(regrlb)
## W = 0.93208, p-value = 0.5014
##UJI ASUMSI: Non-heteroskedastisitas
#Uji Glejser
e=resid(regrlb) #mencari residual
e
## 1 2 3 4 5 6 7
## -0.2980857 -0.2141744 0.4714083 -0.4745650 -0.9524106 0.4760829 0.9274363
## 8 9
## 0.7466056 -0.6822975
ae=abs(e) #absolute residual
ae
## 1 2 3 4 5 6 7 8
## 0.2980857 0.2141744 0.4714083 0.4745650 0.9524106 0.4760829 0.9274363 0.7466056
## 9
## 0.6822975
regae=lm(ae1~X1+X2+X3,data=data)
summary(regae)
##
## Call:
## lm(formula = ae1 ~ X1 + X2 + X3, data = data)
##
## Residuals:
## 1 2 3 4 5 6 7 8
## 0.21222 0.67315 0.58198 0.05159 -1.04846 0.69574 -0.39456 -1.26770
## 9
## 0.49604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.18081 4.52066 -0.482 0.650
## X1 0.02254 0.03403 0.662 0.537
## X2 -0.03484 0.52375 -0.067 0.950
## X3 0.07521 0.17257 0.436 0.681
##
## Residual standard error: 0.9415 on 5 degrees of freedom
## Multiple R-squared: 0.2099, Adjusted R-squared: -0.2641
## F-statistic: 0.4428 on 3 and 5 DF, p-value: 0.7328
#Uji Breush-Pagan
bptest(regrlb)
##
## studentized Breusch-Pagan test
##
## data: regrlb
## BP = 0.52402, df = 3, p-value = 0.9136
##UJI ASUMSI: Non-autokorelasi
#Uji Durbin Watson
dwtest(regrlb)
##
## Durbin-Watson test
##
## data: regrlb
## DW = 1.6446, p-value = 0.1165
## alternative hypothesis: true autocorrelation is greater than 0
#Uji Breush-Godfrey
bgtest(regrlb)
##
## Breusch-Godfrey test for serial correlation of order up to 1
##
## data: regrlb
## LM test = 0.51249, df = 1, p-value = 0.4741
##Hasil Regresi Linear Berganda Model 4
summary(regrlb)
##
## 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