R Markdown
#Memanggil Package
library(cluster)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 1.0.1
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library("readxl")
#Memanggil Data
data<-read_excel("C:/Users/ACER/Downloads/dataanreg.xlsx")
#Melihat Tabel
View(data)
#Melihat Data Frame
data1 <-data.frame(data)
data1
## X1 X2 Y
## 1 0.9189 -10 3.1280
## 2 0.9189 0 2.4270
## 3 0.9189 10 1.9400
## 4 0.9189 20 1.5860
## 5 0.9189 30 1.3250
## 6 0.9189 40 1.1260
## 7 0.9189 50 0.9694
## 8 0.9189 60 0.8473
## 9 0.9189 70 0.7481
## 10 0.9189 80 0.6671
## 11 0.7547 -10 2.2700
## 12 0.7547 0 1.8190
## 13 0.7547 10 1.4890
## 14 0.7547 20 1.2460
## 15 0.7547 30 1.0620
## 16 0.7547 40 0.9160
## 17 0.7547 50 0.8005
## 18 0.7547 60 0.7091
## 19 0.7547 70 0.6345
## 20 0.7547 80 0.5715
## 21 0.5685 -10 1.5930
## 22 0.5685 0 1.3240
## 23 0.5685 10 1.1180
## 24 0.5685 20 0.9576
## 25 0.5685 30 0.8302
## 26 0.5685 40 0.7282
## 27 0.5685 50 0.6470
## 28 0.5685 60 0.5784
## 29 0.5685 70 0.5219
## 30 0.5685 80 0.4735
## 31 0.3610 -10 1.1610
## 32 0.3610 0 0.9925
## 33 0.3610 10 0.8601
## 34 0.3610 20 0.7523
## 35 0.3610 30 0.6663
## 36 0.3610 40 0.5940
## 37 0.3610 50 0.5338
## 38 0.3610 60 0.4804
## 39 0.3610 70 0.4361
## 40 0.3610 80 0.4016
n=40
#Melihat Summary()dari Data
summary(data1)
## X1 X2 Y
## Min. :0.3610 Min. :-10 Min. :0.4016
## 1st Qu.:0.5166 1st Qu.: 10 1st Qu.:0.6439
## Median :0.6616 Median : 35 Median :0.8537
## Mean :0.6508 Mean : 35 Mean :1.0483
## 3rd Qu.:0.7957 3rd Qu.: 60 3rd Qu.:1.2655
## Max. :0.9189 Max. : 80 Max. :3.1280
#Melihat data teratas
head(data1)
## X1 X2 Y
## 1 0.9189 -10 3.128
## 2 0.9189 0 2.427
## 3 0.9189 10 1.940
## 4 0.9189 20 1.586
## 5 0.9189 30 1.325
## 6 0.9189 40 1.126
#Melihat data terendah
tail(data1)
## X1 X2 Y
## 35 0.361 30 0.6663
## 36 0.361 40 0.5940
## 37 0.361 50 0.5338
## 38 0.361 60 0.4804
## 39 0.361 70 0.4361
## 40 0.361 80 0.4016
#Membuat Matriks dengan Fungsi Pairs
pairs(data1)
pairs(data1, lower.panel=NULL)
#Mencari Model Regresi Linear Berganda
model <- lm(data1$Y ~ data1$X1 + data1$X2, data = data1)
model
##
## Call:
## lm(formula = data1$Y ~ data1$X1 + data1$X2, data = data1)
##
## Coefficients:
## (Intercept) data1$X1 data1$X2
## 0.67944 1.40733 -0.01563
#Mencari Summary dari Model Regresi Linear Berganda
summary(model)
##
## Call:
## lm(formula = data1$Y ~ data1$X1 + data1$X2, data = data1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.22179 -0.18102 -0.08439 0.09111 0.99908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.679439 0.143532 4.734 3.20e-05 ***
## data1$X1 1.407331 0.196925 7.147 1.81e-08 ***
## data1$X2 -0.015629 0.001428 -10.948 3.67e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2593 on 37 degrees of freedom
## Multiple R-squared: 0.822, Adjusted R-squared: 0.8124
## F-statistic: 85.46 on 2 and 37 DF, p-value: 1.351e-14
#Mencari Selang Kepercayaan
confint(model)
## 2.5 % 97.5 %
## (Intercept) 0.38861534 0.97026256
## data1$X1 1.00832229 1.80633944
## data1$X2 -0.01852144 -0.01273625
#Mencari anova
anova(model)
## Analysis of Variance Table
##
## Response: data1$Y
## Df Sum Sq Mean Sq F value Pr(>F)
## data1$X1 1 3.4349 3.4349 51.073 1.810e-08 ***
## data1$X2 1 8.0606 8.0606 119.851 3.674e-13 ***
## Residuals 37 2.4885 0.0673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Membuat vektor satuan untuk digabungkan dengan variabel x1 dan x2
a = c(rep(1,40))
U=cbind(a,data1$X1,data1$X2)
U
## a
## [1,] 1 0.9189 -10
## [2,] 1 0.9189 0
## [3,] 1 0.9189 10
## [4,] 1 0.9189 20
## [5,] 1 0.9189 30
## [6,] 1 0.9189 40
## [7,] 1 0.9189 50
## [8,] 1 0.9189 60
## [9,] 1 0.9189 70
## [10,] 1 0.9189 80
## [11,] 1 0.7547 -10
## [12,] 1 0.7547 0
## [13,] 1 0.7547 10
## [14,] 1 0.7547 20
## [15,] 1 0.7547 30
## [16,] 1 0.7547 40
## [17,] 1 0.7547 50
## [18,] 1 0.7547 60
## [19,] 1 0.7547 70
## [20,] 1 0.7547 80
## [21,] 1 0.5685 -10
## [22,] 1 0.5685 0
## [23,] 1 0.5685 10
## [24,] 1 0.5685 20
## [25,] 1 0.5685 30
## [26,] 1 0.5685 40
## [27,] 1 0.5685 50
## [28,] 1 0.5685 60
## [29,] 1 0.5685 70
## [30,] 1 0.5685 80
## [31,] 1 0.3610 -10
## [32,] 1 0.3610 0
## [33,] 1 0.3610 10
## [34,] 1 0.3610 20
## [35,] 1 0.3610 30
## [36,] 1 0.3610 40
## [37,] 1 0.3610 50
## [38,] 1 0.3610 60
## [39,] 1 0.3610 70
## [40,] 1 0.3610 80
#Mencari b0, b1, dan b2 untuk persamaan regresi berganda
b = solve(t(U)%*%U)%*%t(U)%*%data1$Y
b
## [,1]
## a 0.67943895
## 1.40733087
## -0.01562885
#Memasukan nilai-nilai dari b ke peubah b0, b1, dan b2
b0 = b[1]
b0
## [1] 0.679439
b1 = b[2]
b1
## [1] 1.407331
b2= b[3]
b2
## [1] -0.01562885
#Korelasi AntarVariabel
#a. Korelasi Variabel Y dengan X1
cor(data1$Y, data$X1)
## [1] 0.4956134
#b. Korelasi variabel Y dengan X2
cor(data1$Y, data$X2)
## [1] -0.7592214