INSTAL PACKAGES YANG DIPERLUKAN
library(readxl) #untuk impor .xlsx
library(car) # untuk pengujian analisis## Loading required package: carDatalibrary(lmtest) # untuk membuat model regresi## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericdata2 = read_xlsx("Data Potik.xlsx")
head(data2)## # A tibble: 6 × 5
## `Kabupaten/ Kota` AHH AKB ABH MCK
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ACEH 69.9 19.4 1.58 76.5
## 2 SUMATERA UTARA 69.1 18.3 0.77 87.3
## 3 SUMATERA BARAT 69.5 16.4 0.77 76.2
## 4 RIAU 71.6 15.7 0.71 91.8
## 5 JAMBI 71.2 17.0 1.64 85.0
## 6 SUMATERA SELATAN 69.9 16.8 1.16 82.5#ANALISIS STATISTIK DESKRIPTIF
summary(data2)## Kabupaten/ Kota AHH AKB ABH
## Length:34 Min. :65.06 Min. :10.38 Min. : 0.2600
## Class :character 1st Qu.:68.67 1st Qu.:15.55 1st Qu.: 0.9875
## Mode :character Median :69.96 Median :17.23 Median : 1.7050
## Mean :70.04 Mean :19.74 Mean : 3.3024
## 3rd Qu.:71.53 3rd Qu.:24.30 3rd Qu.: 4.6750
## Max. :74.99 Max. :38.17 Max. :20.3500
## MCK
## Min. :61.74
## 1st Qu.:76.27
## Median :82.33
## Mean :80.92
## 3rd Qu.:85.49
## Max. :93.55#ANALISIS STATISTIK INFERENSIA
x1 <- data2$AKB # mendefinisikan data AKB sebagai variabel x1
x2 <- data2$ABH # mendefinisikan data ABH sebagai variabel x2
x3 <- data2$MCK # mendefinisikan data MCK sebagai variabel x3
y <- data2$AHH # mendefinisikan data Y sebagai variabel y1.Pembentukan model dan pendugaan parameter model
model<- lm(y ~ x1+x2+x3)
model$coefficients ## (Intercept) x1 x2 x3
## 73.50365681 -0.27919661 0.03234640 0.023986612. Pengujian keberartian model
summary(model)##
## Call:
## lm(formula = y ~ x1 + x2 + x3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.24410 -0.94660 -0.06875 1.10792 2.88728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 73.50366 4.83556 15.201 1.23e-15 ***
## x1 -0.27920 0.05565 -5.017 2.22e-05 ***
## x2 0.03235 0.07309 0.443 0.661
## x3 0.02399 0.04878 0.492 0.627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.478 on 30 degrees of freedom
## Multiple R-squared: 0.688, Adjusted R-squared: 0.6568
## F-statistic: 22.05 on 3 and 30 DF, p-value: 9.75e-08UJI HIPOTESIS DENGAN F-STATISTICS (UJI SIMULTAN) UJI HIPOTESIS KOEFISIEN REGRESI DENGAN T-STATISTICS (UJI PARSIAL)
3. Penilaian ketepatan model
summary(model)##
## Call:
## lm(formula = y ~ x1 + x2 + x3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.24410 -0.94660 -0.06875 1.10792 2.88728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 73.50366 4.83556 15.201 1.23e-15 ***
## x1 -0.27920 0.05565 -5.017 2.22e-05 ***
## x2 0.03235 0.07309 0.443 0.661
## x3 0.02399 0.04878 0.492 0.627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.478 on 30 degrees of freedom
## Multiple R-squared: 0.688, Adjusted R-squared: 0.6568
## F-statistic: 22.05 on 3 and 30 DF, p-value: 9.75e-084. Pemeriksaana asumsi
res <- model$residuals # mendefinisikan residual
ks.test(res, "pnorm", mean=mean(res),sd=sd(res)) # Uji Kolmogorov-Smirnov --uji kenormalan##
## Exact one-sample Kolmogorov-Smirnov test
##
## data: res
## D = 0.079896, p-value = 0.9696
## alternative hypothesis: two-sided# BENTUK VARIABEL NILAI ABSOLUT DARI RESIDUAL
abs_res <- abs(res)
# REGRESIKAN SELURUH VARIABEL BEBAS TERHADAP NILAI ABSOLUT DARI RESIDUAL
model_glejser <- lm(abs_res~x1+x2+x3)
summary(model_glejser)##
## Call:
## lm(formula = abs_res ~ x1 + x2 + x3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0862 -0.5511 -0.0331 0.5326 1.6869
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.534740 2.640061 0.203 0.841
## x1 0.004533 0.030381 0.149 0.882
## x2 0.008996 0.039904 0.225 0.823
## x3 0.006287 0.026634 0.236 0.815
##
## Residual standard error: 0.8071 on 30 degrees of freedom
## Multiple R-squared: 0.003005, Adjusted R-squared: -0.09669
## F-statistic: 0.03014 on 3 and 30 DF, p-value: 0.9928round(cor(data2[,c(3,4,5)]),2) # menampilkan tabel korelasi antarvariabel## AKB ABH MCK
## AKB 1.00 0.39 -0.76
## ABH 0.39 1.00 -0.42
## MCK -0.76 -0.42 1.00vif(model)## x1 x2 x3
## 2.369611 1.238668 2.443183