library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(GGally)

## Loading required package: ggplot2

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

library(psych)

## 
## Attaching package: 'psych'

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

library(car)

## Loading required package: carData

## 
## Attaching package: 'car'

## The following object is masked from 'package:psych':
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##     logit

## The following object is masked from 'package:dplyr':
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library(lmtest)

## Loading required package: zoo

## 
## Attaching package: 'zoo'

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

library(corrplot)

## corrplot 0.95 loaded

library(nortest)
library(MASS)

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## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
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##     select

library(readxl)
library(olsrr)

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## Attaching package: 'olsrr'

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library(stargazer)

## 
## Please cite as:

##  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.

##  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer

library(readr)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ lubridate 1.9.4     ✔ tibble    3.2.1
## ✔ purrr     1.0.4     ✔ tidyr     1.3.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ psych::%+%()    masks ggplot2::%+%()
## ✖ psych::alpha()  masks ggplot2::alpha()
## ✖ dplyr::filter() masks stats::filter()
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## ✖ purrr::some()   masks car::some()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(DT)
library(DataExplorer)
library(Kendall)
library(vroom)

## 
## Attaching package: 'vroom'
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##     date_names_lang, date_names_langs, default_locale, fwf_cols,
##     fwf_empty, fwf_positions, fwf_widths, locale, output_column,
##     problems, spec

library(ggplot2)

library(readxl)
data_pmi <- read_excel("C:/Users/MUTHI'AH IFFA/Downloads/projek anreg/projectanreg1.xlsx", sheet = "Data Regresi")
str(data_pmi)

## tibble [37 × 12] (S3: tbl_df/tbl/data.frame)
##  $ Provinsi  : chr [1:37] "Aceh" "Bali" "Banten" "Bengkulu" ...
##  $ Y         : num [1:37] 458 8143 3567 723 1325 ...
##  $ Ytransform: num [1:37] 2.66 3.91 3.55 2.86 3.12 ...
##  $ X1        : num [1:37] 0.294 0.348 0.359 0.343 0.428 0.431 0.413 0.315 0.428 0.364 ...
##  $ X2        : num [1:37] 13.44 3.9 5.77 13.04 10.62 ...
##  $ X3        : num [1:37] 43.8 67.3 70.3 49.2 51.5 ...
##  $ X4        : num [1:37] 74 77.8 74.5 73.4 81.5 ...
##  $ X5        : num [1:37] 3.46 2.81 2.73 2.51 2.13 ...
##  $ X6        : num [1:37] 3642 1817 3656 2096 1200 ...
##  $ X7        : num [1:37] 92.6 95.1 92.5 91.7 95.1 ...
##  $ X8        : num [1:37] 5.75 1.79 6.68 3.11 3.48 6.21 3.13 4.48 6.75 4.78 ...
##  $ X9        : num [1:37] 9.95 9.87 9.55 9.4 10.23 ...

Menghapus kolom Provinsi dan Y yang belum di transformasi

data.pmi <- data_pmi[-1:-2]
str(data.pmi)

## tibble [37 × 10] (S3: tbl_df/tbl/data.frame)
##  $ Ytransform: num [1:37] 2.66 3.91 3.55 2.86 3.12 ...
##  $ X1        : num [1:37] 0.294 0.348 0.359 0.343 0.428 0.431 0.413 0.315 0.428 0.364 ...
##  $ X2        : num [1:37] 13.44 3.9 5.77 13.04 10.62 ...
##  $ X3        : num [1:37] 43.8 67.3 70.3 49.2 51.5 ...
##  $ X4        : num [1:37] 74 77.8 74.5 73.4 81.5 ...
##  $ X5        : num [1:37] 3.46 2.81 2.73 2.51 2.13 ...
##  $ X6        : num [1:37] 3642 1817 3656 2096 1200 ...
##  $ X7        : num [1:37] 92.6 95.1 92.5 91.7 95.1 ...
##  $ X8        : num [1:37] 5.75 1.79 6.68 3.11 3.48 6.21 3.13 4.48 6.75 4.78 ...
##  $ X9        : num [1:37] 9.95 9.87 9.55 9.4 10.23 ...

Statistika Deskriptif

Summary Data

summary(data.pmi)

##    Ytransform          X1               X2              X3        
##  Min.   :0.000   Min.   :0.2350   Min.   : 3.90   Min.   : 24.27  
##  1st Qu.:1.763   1st Qu.:0.3060   1st Qu.: 5.85   1st Qu.: 51.47  
##  Median :2.661   Median :0.3430   Median : 9.68   Median : 69.35  
##  Mean   :2.626   Mean   :0.3405   Mean   :10.07   Mean   : 86.13  
##  3rd Qu.:3.306   3rd Qu.:0.3640   3rd Qu.:13.04   3rd Qu.: 81.01  
##  Max.   :4.899   Max.   :0.4310   Max.   :21.38   Max.   :344.35  
##        X4              X5              X6              X7       
##  Min.   :59.75   Min.   :2.037   Min.   :   48   Min.   :81.41  
##  1st Qu.:71.23   1st Qu.:2.728   1st Qu.:  715   1st Qu.:89.50  
##  Median :73.33   Median :3.037   Median : 1645   Median :91.50  
##  Mean   :72.90   Mean   :3.089   Mean   : 3410   Mean   :90.83  
##  3rd Qu.:74.43   3rd Qu.:3.402   3rd Qu.: 3616   3rd Qu.:93.22  
##  Max.   :83.08   Max.   :5.067   Max.   :19991   Max.   :95.56  
##        X8              X9        
##  Min.   :1.790   Min.   : 6.170  
##  1st Qu.:3.480   1st Qu.: 8.800  
##  Median :4.190   Median : 9.400  
##  Mean   :4.462   Mean   : 9.406  
##  3rd Qu.:5.750   3rd Qu.: 9.950  
##  Max.   :6.750   Max.   :11.490

Eksplorasi Data

Histogram

hist(data.pmi$Ytransform)

hist(data.pmi$X1)

hist(data.pmi$X2)

hist(data.pmi$X3)

hist(data.pmi$X4)

hist(data.pmi$X5)

hist(data.pmi$X6)

hist(data.pmi$X7)

hist(data.pmi$X8)

hist(data.pmi$X9)

Boxplot

boxplot(data.pmi$Ytransform)

par(mfrow = c(3, 3))  # Membagi plotting area menjadi 3 baris x 3 kolom
boxplot(data.pmi$X1, main = "Boxplot Variabel X1", ylab = "Nilai", names = "X1")
boxplot(data.pmi$X2, main = "Boxplot Variabel X2", ylab = "Nilai", names = "X2")
boxplot(data.pmi$X3, main = "Boxplot Variabel X3", ylab = "Nilai", names = "X3")
boxplot(data.pmi$X4, main = "Boxplot Variabel X4", ylab = "Nilai", names = "X4")
boxplot(data.pmi$X5, main = "Boxplot Variabel X5", ylab = "Nilai", names = "X5")
boxplot(data.pmi$X6, main = "Boxplot Variabel X6", ylab = "Nilai", names = "X6")
boxplot(data.pmi$X7, main = "Boxplot Variabel X7", ylab = "Nilai", names = "X7")
boxplot(data.pmi$X8, main = "Boxplot Variabel X8", ylab = "Nilai", names = "X8")
boxplot(data.pmi$X9, main = "Boxplot Variabel X9", ylab = "Nilai", names = "X9")

boxplot(data.pmi$X1, 
        main = "Gini Rasio (X1)", 
        ylab = "Indeks", 
        names = "X1")

boxplot(data.pmi$X2, 
        main = "Rata-rata Penduduk Miskin (X2)", 
        ylab = "Persen", 
        names = "X2")

boxplot(data.pmi$X3, 
        main = "PDRB (X3)", 
        ylab = "Juta", 
        names = "X3")

boxplot(data.pmi$X4, 
        main = "IPM (X4)", 
        ylab = "Indeks", 
        names = "X4")

boxplot(data.pmi$X5, 
        main = "UMP (X5)", 
        ylab = "Juta", 
        names = "X5")

boxplot(data.pmi$X6, 
        main = "Pelatihan Berbasis Kompetensi (X6)", 
        ylab = "Orang", 
        names = "X6")

boxplot(data.pmi$X7, 
        main = "Indeks Pembangunan Gender (X7)", 
        ylab = "Indeks", 
        names = "X7")

boxplot(data.pmi$X8, 
        main = "Tingkat Pengangguran terbuka (X8)", 
        ylab = "Persen", 
        names = "X8")

boxplot(data.pmi$X9, 
        main = "Rata-rata Lama Sekolah (X9)", 
        ylab = "Tahun", 
        names = "X9")

Plot Y vs X

plot(data.pmi$X1, data.pmi$Ytransform)

plot(data.pmi$X2, data.pmi$Ytransform)

plot(data.pmi$X3, data.pmi$Ytransform)

plot(data.pmi$X4, data.pmi$Ytransform)

plot(data.pmi$X5, data.pmi$Ytransform)

plot(data.pmi$X6, data.pmi$Ytransform)

plot(data.pmi$X7, data.pmi$Ytransform)

plot(data.pmi$X8, data.pmi$Ytransform)

plot(data.pmi$X9, data.pmi$Ytransform)

projek anregg

Muthi’ah Iffa Karimah

2025-05-04

Menghapus kolom Provinsi dan Y yang belum di transformasi

Statistika Deskriptif

Summary Data

Eksplorasi Data

Histogram

Boxplot

Plot Y vs X