Komstat Pert 6

#PACKAGE
library(lmtest)

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

library(ggplot2)
library(car)

## Loading required package: carData

#MEMANGGIL DATA
data=read.table(file.choose(), header=T)
data

##     Y  X1 X2
## 1  59  90 70
## 2  60  97 77
## 3  62  97 77
## 4  65  99 77
## 5  69 106 76
## 6  65 110 68
## 7  69 115 78
## 8  70 115 78
## 9  74 118 79
## 10 70 122 80
## 11 75 127 79
## 12 76 127 79
## 13 78 127 79
## 14 81 135 85
## 15 85 140 88

#SCATTER PLOT Y (Hasil belajar mahasiswa) DAN X1 (Tingkat IQ)
ggplot(data, aes(x = X1, y = Y)) +
  geom_point(color = "hotpink", size = 2) +
  geom_smooth(method = "lm", se = FALSE,
              color = "springgreen4", linewidth = 1.2) +
  ggtitle("Scatter Plot Antara Tingkat IQ dan Hasil belajar") +
  ylab("Hasil belajar mahasiswa") +
  xlab("Tingkat IQ") +
  theme(plot.title = element_text(hjust = 0.5))

## `geom_smooth()` using formula = 'y ~ x'

#SCATTER PLOT Y (Hasil belajar mahasiswa) DAN X2 (Motivasi)
ggplot(data, aes(x = X2, y = Y)) +
  geom_point(color = "hotpink", size = 2) +
  geom_smooth(method = "lm", se = FALSE,
              color = "springgreen4", linewidth = 1.2) +
  ggtitle("Scatter Plot Antara Motivasi dan Hasil belajar") +
  ylab("Hasil belajar mahasiswa") +
  xlab("Motivasi") +
  theme(plot.title = element_text(hjust = 0.5))

## `geom_smooth()` using formula = 'y ~ x'

#REGRESI LINIER BERGANDA
model=lm(Y~X1+X2,data=data)
summary(model)

## 
## Call:
## lm(formula = Y ~ X1 + X2, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0542 -0.8326  0.0654  1.4173  2.8417 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.69711    9.43046   0.074    0.942    
## X1           0.42708    0.05444   7.846 4.59e-06 ***
## X2           0.26567    0.16869   1.575    0.141    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.019 on 12 degrees of freedom
## Multiple R-squared:  0.9404, Adjusted R-squared:  0.9304 
## F-statistic: 94.59 on 2 and 12 DF,  p-value: 4.504e-08

#PENDETEKSIAN MULTIKOLINIERITAS
#<10
vif(model)

##       X1       X2 
## 2.290752 2.290752

#PENGUJIAN ASUMSI
bptest(model,varformula=~fitted.values(model), studentize=FALSE)

## 
##  Breusch-Pagan test
## 
## data:  model
## BP = 0.032304, df = 1, p-value = 0.8574

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

## 
##  Durbin-Watson test
## 
## data:  model
## DW = 2.0697, p-value = 0.7751
## alternative hypothesis: true autocorrelation is not 0

shapiro.test(resid(model))

## 
##  Shapiro-Wilk normality test
## 
## data:  resid(model)
## W = 0.96508, p-value = 0.7797