TM3.KOMSTAT.MUHAMMAD ARYANDI HIDAYAT

#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=data.frame(
  Petak=1:12,
  Y=c(34.4,38.7,34.2,41.7,39.0,41.5,40.9,44.0,43.9,47.6,49.7,48.7),
  X1=c(100,110,120,130,140,150,160,170,180,190,200,210),
  X2=c(140,180,150,200,170,220,190,240,200,230,260,220))
data

##    Petak    Y  X1  X2
## 1      1 34.4 100 140
## 2      2 38.7 110 180
## 3      3 34.2 120 150
## 4      4 41.7 130 200
## 5      5 39.0 140 170
## 6      6 41.5 150 220
## 7      7 40.9 160 190
## 8      8 44.0 170 240
## 9      9 43.9 180 200
## 10    10 47.6 190 230
## 11    11 49.7 200 260
## 12    12 48.7 210 220

summary(data)

##      Petak             Y               X1              X2       
##  Min.   : 1.00   Min.   :34.20   Min.   :100.0   Min.   :140.0  
##  1st Qu.: 3.75   1st Qu.:38.92   1st Qu.:127.5   1st Qu.:177.5  
##  Median : 6.50   Median :41.60   Median :155.0   Median :200.0  
##  Mean   : 6.50   Mean   :42.02   Mean   :155.0   Mean   :200.0  
##  3rd Qu.: 9.25   3rd Qu.:44.90   3rd Qu.:182.5   3rd Qu.:222.5  
##  Max.   :12.00   Max.   :49.70   Max.   :210.0   Max.   :260.0

#SCATTER PLOT#
plot(data$X1, data$Y, xlab="X1", ylab="Y",
     main="Scatter Plot X1 dan Y")
abline(lm(data$Y~data$X1), col="lightpink", lwd=3)

plot(data$X2, data$Y, xlab="X2", ylab="Y",
     main="Scatter Plot X2 dan Y")
abline(lm(data$Y~data$X2), col="lightpink", lwd=3)

#RLB#
reg=lm(formula=Y~X1+X2, data=data)
summary(reg)

## 
## Call:
## lm(formula = Y ~ X1 + X2, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8540 -1.0105  0.1800  0.9349  1.6401 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.96176    2.30605   7.355  4.3e-05 ***
## X1           0.07860    0.01965   4.000  0.00311 ** 
## X2           0.06440    0.01958   3.289  0.00940 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.361 on 9 degrees of freedom
## Multiple R-squared:  0.9412, Adjusted R-squared:  0.9281 
## F-statistic: 71.99 on 2 and 9 DF,  p-value: 2.905e-06

#Pendeteksian Multikolinieritas
#<10
vif(reg)

##       X1       X2 
## 2.983051 2.983051

#PENGUJIAN ASUMSI#
bptest(reg, varformula=~fitted.values(reg), data=data, studentize=F)

## 
##  Breusch-Pagan test
## 
## data:  reg
## BP = 0.2226, df = 1, p-value = 0.6371

dwtest(reg, alternative='two.sided')

## 
##  Durbin-Watson test
## 
## data:  reg
## DW = 2.0988, p-value = 0.9478
## alternative hypothesis: true autocorrelation is not 0

shapiro.test(resid(reg))

## 
##  Shapiro-Wilk normality test
## 
## data:  resid(reg)
## W = 0.92837, p-value = 0.3631