p2

library(readxl)
data<-read_xlsx("C:/Users/ASUS R3/Downloads/jatim for anreg/kualitas hidup/DATA FOR ANREG.xlsx") 
data

## # A tibble: 38 × 9
##    WILAYAH       IPM   RLS   HLS   PDRB    PP   UHH  IPLM   TPT
##    <chr>       <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 Pacitan      70.2  70.2  12.7 33149.  9681  74.6  65.2  3.65
##  2 Ponorogo     72.5  72.5  13.8 26314. 10658  75.1  58.1  5.51
##  3 Trenggalek   71.7  71.7  12.6 30681  10465  75.2  84.2  5.37
##  4 Tulungagung  74.6  74.6  13.3 43297. 11565  75.0  58.4  6.65
##  5 Blitar       72.5  72.5  12.6 35812. 11499  75.1  70.0  5.45
##  6 Kediri       74.0  74.0  13.6 30193. 11952  74.8  66.5  6.83
##  7 Malang       72.2  72.2  13.5 47272. 10791  75.1  48.8  6.57
##  8 Lumajang     67.9  67.9  12.2 35178.  9720  74.4  53.9  4.97
##  9 Jember       68.6  68.6  13.5 36837. 10277  74.0  37.7  4.06
## 10 Banyuwangi   72.6  72.6  13.1 58086. 12820  73.9  65.6  5.26
## # ℹ 28 more rows

colnames(data) <- c("Wilayah", "Y", paste0("X", 1:9))
head(data)

## # A tibble: 6 × 9
##   Wilayah         Y    X1    X2     X3    X4    X5    X6    X7
##   <chr>       <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Pacitan      70.2  70.2  12.7 33149.  9681  74.6  65.2  3.65
## 2 Ponorogo     72.5  72.5  13.8 26314. 10658  75.1  58.1  5.51
## 3 Trenggalek   71.7  71.7  12.6 30681  10465  75.2  84.2  5.37
## 4 Tulungagung  74.6  74.6  13.3 43297. 11565  75.0  58.4  6.65
## 5 Blitar       72.5  72.5  12.6 35812. 11499  75.1  70.0  5.45
## 6 Kediri       74.0  74.0  13.6 30193. 11952  74.8  66.5  6.83

n<-nrow(data)

plot(data$X1, data$Y,
     main = "IPM (%) vs RLS (%)",
     xlab = "RLS (tahun)",
     ylab = "IPM")