title: “Analisis Uang Kuartal Provinsi Bali”

author: ‘SOFYAN H. RAHMAWAN UIN MALIKI MALANG’

date: 3/3/2022

output:

html_document:

Dosen Pengampu : Prof. Dr. Suhartono, Mkom

library(readxl)
datainflowbali <- read_excel(path = "bali.xlsx")

## New names:
## * `` -> ...2

datainflowbali

## # A tibble: 11 x 3
##    Tahun ...2    Bali
##    <dbl> <lgl>  <dbl>
##  1  2011 NA     6394.
##  2  2012 NA     8202.
##  3  2013 NA     5066.
##  4  2014 NA    11590.
##  5  2015 NA    13072.
##  6  2016 NA    17914.
##  7  2017 NA    16962.
##  8  2018 NA    18610.
##  9  2019 NA    21422.
## 10  2020 NA    14735.
## 11  2021 NA     7505.

summary(datainflowbali)

##      Tahun        ...2              Bali      
##  Min.   :2011   Mode:logical   Min.   : 5066  
##  1st Qu.:2014   NA's:11        1st Qu.: 7854  
##  Median :2016                  Median :13072  
##  Mean   :2016                  Mean   :12861  
##  3rd Qu.:2018                  3rd Qu.:17438  
##  Max.   :2021                  Max.   :21422

plot(datainflowbali$Bali ~ datainflowbali$Tahun, data = datainflowbali)

cor(datainflowbali$Bali, datainflowbali$Tahun)

## [1] 0.5371528

model <- lm(datainflowbali$Bali ~ datainflowbali$Tahun)

summary(model)

anova(model)

plot(datainflowbali$Bali ~ datainflowbali$Tahun, data = datainflowbali, col = "red", pch = 20, cex = 1.8, main = "Data Inflow Bali")
abline(model)

plot(cooks.distance(model), pch = 16, col = "blue")

plot(model)

AIC(model)

## [1] 222.0543

BIC(model)

## [1] 223.248

head(predict(model), n = 11)

##         1         2         3         4         5         6         7         8 
##  8375.650  9272.745 10169.839 11066.934 11964.028 12861.122 13758.217 14655.311 
##         9        10        11 
## 15552.405 16449.500 17346.594

plot(head(predict(model), n = 10))

head(resid(model), n = 11)

##          1          2          3          4          5          6          7 
## -1981.3013 -1070.2783 -5103.3834   523.0313  1107.6271  5052.5809  3203.8893 
##          8          9         10         11 
##  3954.2965  5869.2993 -1714.3461 -9841.4152

coef(model)

##          (Intercept) datainflowbali$Tahun 
##        -1795681.0701             897.0943

datainflowbali$residuals <- model$residuals

datainflowbali$predicted <- model$fitted.values

datainflowbali

## # A tibble: 11 x 5
##    Tahun ...2    Bali residuals predicted
##    <dbl> <lgl>  <dbl>     <dbl>     <dbl>
##  1  2011 NA     6394.    -1981.     8376.
##  2  2012 NA     8202.    -1070.     9273.
##  3  2013 NA     5066.    -5103.    10170.
##  4  2014 NA    11590.      523.    11067.
##  5  2015 NA    13072.     1108.    11964.
##  6  2016 NA    17914.     5053.    12861.
##  7  2017 NA    16962.     3204.    13758.
##  8  2018 NA    18610.     3954.    14655.
##  9  2019 NA    21422.     5869.    15552.
## 10  2020 NA    14735.    -1714.    16449.
## 11  2021 NA     7505.    -9841.    17347.

scatter.smooth(x=datainflowbali$Tahun, y=datainflowbali$Bali, main="Tahun ~ Bali")

boxplot(datainflowbali$Bali, main="Bali", boxplot.stats(datainflowbali$Bali)$out)

  plot(density(datainflowbali$Bali), main="Bali Plot: Inflow", ylab="Frequency")

coefs <- coef(model)
plot(Bali ~ Tahun, data = datainflowbali)
abline(coefs) 
text(x = 12, y = 10, paste('expression = ', round(coefs[1], 2),  '+', round(coefs[2], 2), '*Bali'))

cor.test(datainflowbali$Tahun, datainflowbali$Bali)

## 
##  Pearson's product-moment correlation
## 
## data:  datainflowbali$Tahun and datainflowbali$Bali
## t = 1.9105, df = 9, p-value = 0.08839
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.09254129  0.85993549
## sample estimates:
##       cor 
## 0.5371528