TM.Komstat_Aliefia Novita Putri

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

#SCATTER PLOT Y (Hasil panen padi) DAN X1 (Dosis pupuk)
ggplot(data, aes(x = X1, y = Y)) +
  geom_point(color = "tomato", size = 2) +
  geom_smooth(method = "lm", se = FALSE,
              color = "seagreen", linewidth = 1.2) +
  ggtitle("Scatter Plot Antara Dosis Pupuk dan Hasil Panen Padi") +
  ylab("Hasil Panen Padi (kuintal/ha)") +
  xlab("Dosis Pupuk (kg/ha)") +
  theme(plot.title = element_text(hjust = 0.5))

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

#SCATTER PLOT Y (Hasil panen padi) DAN X2 (Curah hujan)
ggplot(data, aes(x = X2, y = Y)) +
  geom_point(color = "tomato", size = 2) +
  geom_smooth(method = "lm", se = FALSE,
              color = "seagreen", linewidth = 1.2) +
  ggtitle("Scatter Plot Antara Curah Hujan dan Hasil Panen Padi") +
  ylab("Hasil Panen Padi (kuintal/ha)") +
  xlab("Curah Hujan (mm/bulan)") +
  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 
## -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(model)

##       X1       X2 
## 2.983051 2.983051

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

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

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

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

shapiro.test(resid(model))

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

#PENGUJIAN SIGNIFIKANSI SECARA SIMULTAN (UJI F)
n=nrow(data)              #banyak pengamatan
k=2                       #banyak variabel independen

#JUMLAH KUADRAT
Y=data$Y
Yhat=fitted(model)
SST=sum((Y-mean(Y))^2)    #Total
SSR=sum((Yhat-mean(Y))^2) #Regresi
SSE=sum((Y-Yhat)^2)       #Error

#KOEFISIEN DETERMINASI
R2=SSR/SST
R2

## [1] 0.9411695

R2adj=1-((SSE/(n-k-1))/(SST/(n-1)))
R2adj

## [1] 0.9280961

#STATISTIK UJI F HITUNG
MSR=SSR/k
MSE=SSE/(n-k-1)
Fhit=MSR/MSE
Fhit

## [1] 71.99094

#F TABEL
alpha=0.05
Ftab=qf(1-alpha, df1=k, df2=n-k-1)
Ftab

## [1] 4.256495

#KEPUTUSAN
ifelse(Fhit>Ftab, "Tolak H0", "Gagal Tolak H0")

## [1] "Tolak H0"

#P-VALUE (PEMBANDING)
pval=pf(Fhit, k, n-k-1, lower.tail=FALSE)
pval

## [1] 2.905437e-06