# Memanggil Package
library(stats)
library(car)
## Loading required package: carData
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(zoo)
# Memanggil Data
data=read.table(file.choose(),header=TRUE)
data
## Provinsi IPM PPM TPT PRT
## 1 Aceh 74.03 12.64 5.75 81.10
## 2 Sumatera_Utara 74.02 7.19 5.60 85.73
## 3 Sumatera_Barat 74.49 5.42 5.75 72.82
## 4 Riau 74.79 6.36 3.70 86.32
## 5 Jambi 73.43 7.26 4.48 83.97
## 6 Sumatera_Selatan 72.30 10.51 3.86 82.36
## 7 Bengkulu 73.39 12.52 3.11 83.01
## 8 Lampung 71.81 10.62 4.19 85.44
## 9 Kep._Bangka_Belitung 73.33 5.08 4.63 94.16
## 10 Kep._Riau 77.97 4.78 6.39 91.23
## 11 DKI_Jakarta 83.08 4.14 6.21 94.01
## 12 Jawa_Barat 74.43 7.08 6.75 75.10
## 13 Jawa_Tengah 73.88 9.58 4.78 86.72
## 14 DI_Yogyakarta 81.55 10.40 3.48 96.71
## 15 Jawa_Timur 74.09 9.56 4.19 85.56
## 16 Banten 74.48 5.70 6.68 87.93
## 17 Bali 77.76 3.80 1.79 96.83
## 18 Nusa_Tenggara_Barat 70.93 11.91 2.73 86.80
## 19 Nusa_Tenggara_Timur 67.39 19.02 3.02 77.66
## 20 Kalimantan_Barat 70.13 6.25 4.86 81.56
## 21 Kalimantan_Tengah 72.73 5.26 4.01 78.15
## 22 Kalimantan_Selatan 73.03 4.02 4.20 83.70
## 23 Kalimantan_Timur 78.83 5.51 5.14 91.65
## 24 Kalimantan_Utara 73.02 5.38 3.90 85.17
## 25 Sulawesi_Utara 75.03 6.70 5.85 87.31
## 26 Sulawesi_Tengah 71.56 11.04 2.94 77.40
## 27 Sulawesi_Selatan 74.05 7.77 4.19 93.83
## 28 Sulawesi_Tenggara 73.48 10.63 3.09 90.62
## 29 Gorontalo 71.23 8.37 3.13 82.99
## 30 Sulawesi_Barat 68.20 10.71 2.68 82.52
## 31 Maluku 71.57 15.78 6.11 80.51
## 32 Maluku_Utara 71.03 6.03 4.03 82.32
## 33 Papua_Barat 67.02 21.09 4.13 79.00
## 34 Papua_Barat_Daya 68.63 16.95 6.48 77.84
## 35 Papua 73.00 18.09 6.48 80.57
## 36 Papua_Selatan 67.90 19.35 4.05 60.85
## 37 Papua_Tengah 59.75 27.60 2.75 41.44
## 38 Papua_Pegunungan 53.42 29.66 1.32 12.61
# Statistika Deskriptif
summary(data)
## Provinsi IPM PPM TPT
## Length :38 Min. :53.42 Min. : 3.800 Min. :1.320
## N.unique :38 1st Qu.:71.08 1st Qu.: 5.782 1st Qu.:3.217
## N.blank : 0 Median :73.18 Median : 8.965 Median :4.190
## Min.nchar: 4 Mean :72.39 Mean :10.520 Mean :4.380
## Max.nchar:20 3rd Qu.:74.34 3rd Qu.:12.367 3rd Qu.:5.713
## Max. :83.08 Max. :29.660 Max. :6.750
## PRT
## Min. :12.61
## 1st Qu.:79.38
## Median :83.36
## Mean :81.14
## 3rd Qu.:87.18
## Max. :96.83
# Scatterplot per variabel
plot(data$PPM,data$IPM)
abline(lm(data$IPM~data$PPM),col="purple",lwd=3)
plot(data$TPT,data$IPM)
abline(lm(data$IPM~data$TPT),col="purple",lwd=3)
plot(data$PRT,data$IPM)
abline(lm(data$IPM~data$PRT),col="purple",lwd=3)
# Model Awal
reg=lm(formula=IPM~PPM+TPT+PRT,data=data)
summary(reg)
##
## Call:
## lm(formula = IPM ~ PPM + TPT + PRT, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6693 -1.3153 -0.0973 1.1655 6.5839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55.97748 4.04937 13.824 1.63e-15 ***
## PPM -0.24084 0.09221 -2.612 0.0133 *
## TPT 0.62372 0.27933 2.233 0.0322 *
## PRT 0.19980 0.03895 5.130 1.17e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.263 on 34 degrees of freedom
## Multiple R-squared: 0.8226, Adjusted R-squared: 0.807
## F-statistic: 52.56 on 3 and 34 DF, p-value: 7.388e-13
# Pendeteksian Multikolinieritas
vif(reg)
## PPM TPT PRT
## 2.480832 1.127737 2.490226
# Pengujian Asumsi
bptest(reg,varformula=~fitted.values(reg),studentize=FALSE)
##
## Breusch-Pagan test
##
## data: reg
## BP = 2.841, df = 1, p-value = 0.09188
dwtest(reg,alternative='two.sided')
##
## Durbin-Watson test
##
## data: reg
## DW = 1.7305, p-value = 0.2731
## alternative hypothesis: true autocorrelation is not 0
shapiro.test(resid(reg))
##
## Shapiro-Wilk normality test
##
## data: resid(reg)
## W = 0.94432, p-value = 0.05793