Latihan Soal Susunan Data

Nama : Muhammad Hafidlul Qolbi

NIM : 220605110063

Kelas : C

Mata Kuliah : Kalkulus

Dosen Pengampuh : Prof. Dr. Suhartono, M.Kom

Jurusan : Teknik Informatika

Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Latihan Soal Susunan Data!!

library(MASS)
lm.fit <- lm(medv~lstat, data=Boston)
anova(lm.fit)

## Analysis of Variance Table
## 
## Response: medv
##            Df Sum Sq Mean Sq F value    Pr(>F)    
## lstat       1  23244 23243.9  601.62 < 2.2e-16 ***
## Residuals 504  19472    38.6                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(lm.fit)

## 
## Call:
## lm(formula = medv ~ lstat, data = Boston)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -15.168  -3.990  -1.318   2.034  24.500 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 34.55384    0.56263   61.41   <2e-16 ***
## lstat       -0.95005    0.03873  -24.53   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.216 on 504 degrees of freedom
## Multiple R-squared:  0.5441, Adjusted R-squared:  0.5432 
## F-statistic: 601.6 on 1 and 504 DF,  p-value: < 2.2e-16

shapiro.test(residuals(lm.fit))

## 
##  Shapiro-Wilk normality test
## 
## data:  residuals(lm.fit)
## W = 0.87857, p-value < 2.2e-16

library(lmtest)

## Warning: package 'lmtest' was built under R version 4.2.2

## Loading required package: zoo

## Warning: package 'zoo' was built under R version 4.2.2

## 
## Attaching package: 'zoo'

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

bptest(lm.fit)

## 
##  studentized Breusch-Pagan test
## 
## data:  lm.fit
## BP = 15.497, df = 1, p-value = 8.262e-05

dwtest(lm.fit, alternative = "two.sided")

## 
##  Durbin-Watson test
## 
## data:  lm.fit
## DW = 0.8915, p-value < 2.2e-16
## alternative hypothesis: true autocorrelation is not 0

sres <- rstandard(lm.fit)
sres[which(abs(sres)>2)]

##        99       142       162       163       164       167       181       187 
##  2.038479  2.037877  2.758553  2.787466  3.000496  3.058318  2.002509  3.172448 
##       196       204       205       215       225       226       229       234 
##  2.947239  2.833127  2.933544  2.788670  2.286679  3.199842  2.559571  2.822187 
##       257       258       262       263       268       281       283       284 
##  2.000582  3.274417  2.488441  3.201252  3.627671  2.325519  2.307994  2.976149 
##       369       370       371       372       373       375       413       506 
##  2.991366  3.062883  2.945717  3.946264  3.847133  2.498416  2.600516 -2.443658

cooksD <- cooks.distance(lm.fit)
p50 <- qf(0.5, df1=2, df2=560-2)
any(cooksD>p50)

## [1] FALSE

Sumber Referensi:

https://bookdown.org/moh_rosidi2610/Metode_Numerik/datamod.html