Import Library

Buat Tabel Data

data <- data.frame(
  Age = c(30.83, 58.67, 24.50, 27.83, 20.17),
  Debt = c(0.000, 4.460, 0.500, 1.540, 5.625),
  YearsEmployed = c(1.25, 3.75, 1.00, 3.00, 1.71),
  CreditScore = c(1, 0, 0, 1, 0),
  ZipCode = c(202, 43, 280, 820, 120),
  Income = c(0, 560, 824, 3, 0)
)

# Tampilkan Data
print(data)
##     Age  Debt YearsEmployed CreditScore ZipCode Income
## 1 30.83 0.000          1.25           1     202      0
## 2 58.67 4.460          3.75           0      43    560
## 3 24.50 0.500          1.00           0     280    824
## 4 27.83 1.540          3.00           1     820      3
## 5 20.17 5.625          1.71           0     120      0

Analisis Korelasi

cor_matrix <- cor(data)
print(cor_matrix)
##                      Age       Debt YearsEmployed CreditScore    ZipCode
## Age            1.0000000  0.2470022     0.7499255  -0.1842478 -0.3533692
## Debt           0.2470022  1.0000000     0.4661460  -0.6073564 -0.4191343
## YearsEmployed  0.7499255  0.4661460     1.0000000  -0.0131061  0.1516091
## CreditScore   -0.1842478 -0.6073564    -0.0131061   1.0000000  0.6468461
## ZipCode       -0.3533692 -0.4191343     0.1516091   0.6468461  1.0000000
## Income         0.3452272 -0.1158264    -0.0205566  -0.6460998 -0.3108720
##                   Income
## Age            0.3452272
## Debt          -0.1158264
## YearsEmployed -0.0205566
## CreditScore   -0.6460998
## ZipCode       -0.3108720
## Income         1.0000000

Analisis Kovarians

cov_matrix <- cov(data)
print(cov_matrix)
##                        Age        Debt YearsEmployed CreditScore     ZipCode
## Age             231.361400    9.345663      13.50668     -1.5350  -1653.6325
## Debt              9.345663    6.187675       1.37300     -0.8275   -320.7613
## YearsEmployed    13.506675    1.373000       1.40207     -0.0085     55.2300
## CreditScore      -1.535000   -0.827500      -0.00850      0.3000    109.0000
## ZipCode       -1653.632500 -320.761250      55.23000    109.0000  94652.0000
## Income         2046.972500 -112.313750      -9.48850   -137.9500 -37282.7500
##                    Income
## Age             2046.9725
## Debt            -112.3137
## YearsEmployed     -9.4885
## CreditScore     -137.9500
## ZipCode       -37282.7500
## Income        151957.8000

Analisis Eigenvalues dan Eigenvectors

eigen_result <- eigen(cov_matrix)

# Tampilkan Eigenvalues
print(eigen_result$values)
## [1]  1.703650e+05  7.628965e+04  1.895450e+02  4.846672e+00  9.854340e-13
## [6] -9.184771e-12
# Tampilkan Eigenvectors
print(eigen_result$vectors)
##               [,1]          [,2]         [,3]         [,4]          [,5]
## [1,] -0.0150869635 -0.0076018969  0.996347558  0.049593430  0.000000e+00
## [2,] -0.0002417190 -0.0044232590  0.033835410 -0.960151142 -1.861317e-01
## [3,]  0.0001920411  0.0005929174  0.076252758 -0.234650168  1.700893e-01
## [4,]  0.0010091662  0.0004822185  0.006905031  0.143439747 -9.676898e-01
## [5,]  0.4419833100  0.8969119593  0.013525938 -0.003224614 -1.292501e-05
## [6,] -0.8968957707  0.4421210231 -0.010079418 -0.002053372 -1.008609e-03
##               [,6]
## [1,]  0.0674287000
## [2,]  0.2056704167
## [3,] -0.9540409084
## [4,] -0.2072498598
## [5,]  0.0025182585
## [6,] -0.0003861599