Project Analisis Faktor Teknik Peubah Ganda
Kelompok 7 - Sandra Berliana Putri G1401221021
2024-10-21
#Packages
library(readxl)
library(psych)## Warning: package 'psych' was built under R version 4.5.3library(ggcorrplot)## Warning: package 'ggcorrplot' was built under R version 4.5.3## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.5.3##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha#Data
data_sales <- read_excel("C:\\Users\\ASUS\\Downloads\\Data selespeople.xlsx", sheet = "Sheet1")
head(data_sales)## # A tibble: 6 × 8
## Salesperson `Sales growth (x₁)` Sales profitability (…¹ New-account sales (x…²
## <dbl> <dbl> <dbl> <dbl>
## 1 1 93 96 97.8
## 2 2 88.8 91.8 96.8
## 3 3 95 100. 99
## 4 4 101. 104. 107.
## 5 5 102 108. 103
## 6 6 95.8 97.5 99.3
## # ℹ abbreviated names: ¹`Sales profitability (x₂)`, ²`New-account sales (x₃)`
## # ℹ 4 more variables: `Creativity test (x₄)` <dbl>,
## # `Mechanical reasoning test (x₅)` <dbl>,
## # `Abstract reasoning test (x₆)` <dbl>, `Mathematics test (x₇)` <dbl>data_sales <- data_sales[,-1]#Standarisasi Akibat satuan dari tiap peubah berbeda (ada indeks dan skor) sehingga perlu dilakukan stan
data_scale <- data.frame(scale(data_sales))
data_scale## Sales.growth..x.. Sales.profitability..x.. New.account.sales..x..
## 1 -0.79538303 -1.04272665 -1.063193559
## 2 -1.36779714 -1.45642889 -1.275407843
## 3 -0.52280489 -0.61917435 -0.808536419
## 4 0.33581628 -0.27442249 0.846734990
## 5 0.43121863 0.11957965 0.040320714
## 6 -0.41377363 -0.89497585 -0.744872134
## 7 -0.45466035 -0.69797478 -0.808536419
## 8 1.63056248 1.51828722 2.650556399
## 9 0.54024989 0.16882991 0.210092140
## 10 1.08540618 1.37053642 -0.171893569
## 11 0.60839442 0.31658071 0.252534997
## 12 0.09049594 0.51358178 -0.532657851
## 13 0.63565224 0.58253216 0.889177847
## 14 0.09049594 -0.10697158 -0.108229284
## 15 0.15864048 0.04077922 -0.002122143
## 16 -2.36270738 -1.28897798 -1.657393553
## 17 0.33581628 -0.12667169 -0.002122143
## 18 0.60839442 0.41508125 0.146427855
## 19 -0.48191816 -0.22517222 0.040320714
## 20 0.09049594 -0.12667169 0.740627849
## 21 -1.40868386 -1.11167702 -1.487622126
## 22 0.06323813 0.82878349 0.316199282
## 23 -1.54497294 -1.38747852 -1.487622126
## 24 0.88097257 0.73028296 0.528413565
## 25 1.11266400 1.41978669 1.313606414
## 26 -0.75449631 -0.45172345 -1.063193559
## 27 1.08540618 1.12428509 0.952842132
## 28 1.08540618 1.32128616 0.422306424
## 29 -0.89078539 -1.55492942 -0.638764993
## 30 1.01726165 1.41978669 0.358642139
## 31 0.97637492 1.27203589 1.631927839
## 32 -1.43594168 -1.35792836 -1.275407843
## 33 -0.38651581 -0.32367275 -0.490214994
## 34 -0.61820724 -1.19047745 -0.808536419
## 35 1.04451946 1.46903696 1.631927839
## 36 1.04451946 0.87803376 0.889177847
## 37 -0.93167211 -0.69797478 0.146427855
## 38 0.43121863 -0.84572558 0.103984999
## 39 1.28983979 1.54783738 1.207499272
## 40 1.08540618 1.22278562 0.846734990
## 41 0.49936317 0.26733045 0.210092140
## 42 -0.86352757 -0.40247318 -0.744872134
## 43 0.54024989 0.71058285 0.846734990
## 44 -2.11738705 -1.89968129 -1.381514984
## 45 -0.55006270 -0.47142355 -0.638764993
## 46 0.63565224 0.53328189 1.695592124
## 47 -1.27239479 -1.04272665 -1.169300701
## 48 -1.98109797 -1.65342996 -1.805943551
## 49 0.74468350 0.28703055 0.783070705
## 50 0.97637492 1.17353536 0.464749280
## Creativity.test..x.. Mechanical.reasoning.test..x..
## 1 -0.56200418 -0.64405961
## 2 -1.06831426 -1.23493999
## 3 -0.81515922 -0.64405961
## 4 0.45061597 -0.05317923
## 5 -0.30884915 0.24226095
## 6 -0.30884915 -0.05317923
## 7 -0.56200418 -0.64405961
## 8 1.71639115 1.71946190
## 9 -0.30884915 0.83314133
## 10 0.70377100 1.12858152
## 11 0.19746093 0.83314133
## 12 -0.30884915 1.12858152
## 13 1.21008108 0.83314133
## 14 -0.81515922 -1.23493999
## 15 0.45061597 -1.23493999
## 16 -1.06831426 -1.53038018
## 17 -0.05569411 -0.64405961
## 18 -0.05569411 -0.05317923
## 19 -1.57462433 -0.05317923
## 20 1.46323612 0.83314133
## 21 -0.30884915 -0.64405961
## 22 -1.57462433 -0.93949980
## 23 -0.56200418 -1.53038018
## 24 0.19746093 0.24226095
## 25 1.21008108 1.42402171
## 26 -0.30884915 0.24226095
## 27 0.70377100 0.53770114
## 28 -0.30884915 0.53770114
## 29 -0.81515922 -1.23493999
## 30 -0.56200418 0.83314133
## 31 1.71639115 0.24226095
## 32 0.45061597 -0.93949980
## 33 -1.06831426 0.24226095
## 34 -0.30884915 -0.64405961
## 35 1.71639115 0.83314133
## 36 -0.81515922 -0.34861942
## 37 1.71639115 0.53770114
## 38 0.45061597 -0.64405961
## 39 0.95692604 1.42402171
## 40 0.70377100 1.71946190
## 41 -0.56200418 0.83314133
## 42 0.45061597 0.24226095
## 43 1.46323612 1.71946190
## 44 -2.58724448 -2.71214093
## 45 -1.06831426 0.53770114
## 46 1.71639115 -0.34861942
## 47 -1.06831426 0.24226095
## 48 -0.81515922 -1.82582037
## 49 0.70377100 -0.64405961
## 50 0.19746093 0.53770114
## Abstract.reasoning.test..x.. Mathematics.test..x..
## 1 -0.7291026 -0.92619772
## 2 -0.2617291 -1.40068425
## 3 -0.7291026 -0.35681387
## 4 0.6730178 -0.07212195
## 5 0.6730178 0.21256997
## 6 0.2056443 -0.83130041
## 7 -0.7291026 -0.45171118
## 8 2.0751381 2.01561880
## 9 1.1403912 0.11767266
## 10 0.2056443 0.87685112
## 11 0.6730178 0.21256997
## 12 -1.1964760 0.11767266
## 13 0.2056443 0.40236458
## 14 0.2056443 0.40236458
## 15 -1.1964760 0.40236458
## 16 -2.5985964 -1.30578694
## 17 0.2056443 0.21256997
## 18 0.2056443 0.49726189
## 19 1.1403912 0.02277535
## 20 0.2056443 -0.26191657
## 21 -1.6638495 -1.40068425
## 22 0.2056443 1.16154304
## 23 -1.6638495 -1.30578694
## 24 0.6730178 0.68705650
## 25 0.6730178 0.87685112
## 26 -1.6638495 -0.64150580
## 27 0.6730178 0.87685112
## 28 0.2056443 1.82582419
## 29 1.1403912 -1.21088964
## 30 0.2056443 1.35133765
## 31 -0.2617291 1.25644034
## 32 -1.1964760 -1.87517079
## 33 0.2056443 -0.26191657
## 34 0.2056443 -1.02109502
## 35 -0.2617291 1.16154304
## 36 1.6077647 1.63602957
## 37 -1.1964760 -1.11599233
## 38 1.6077647 -0.16701926
## 39 0.6730178 1.06664573
## 40 0.6730178 0.68705650
## 41 1.1403912 0.21256997
## 42 -2.1312230 -0.64150580
## 43 -0.2617291 0.21256997
## 44 -0.7291026 -1.40068425
## 45 0.2056443 -0.54660849
## 46 0.6730178 0.68705650
## 47 0.2056443 -1.49558156
## 48 -1.1964760 -1.97006809
## 49 0.6730178 0.59215920
## 50 0.2056443 0.87685112cov_scale<- cov(data_scale)
cov_scale## Sales.growth..x.. Sales.profitability..x..
## Sales.growth..x.. 1.0000000 0.9219691
## Sales.profitability..x.. 0.9219691 1.0000000
## New.account.sales..x.. 0.8840023 0.8398304
## Creativity.test..x.. 0.5720363 0.5383886
## Mechanical.reasoning.test..x.. 0.7080738 0.7461893
## Abstract.reasoning.test..x.. 0.6744073 0.4582909
## Mathematics.test..x.. 0.9273116 0.9423039
## New.account.sales..x.. Creativity.test..x..
## Sales.growth..x.. 0.8840023 0.5720363
## Sales.profitability..x.. 0.8398304 0.5383886
## New.account.sales..x.. 1.0000000 0.7003630
## Creativity.test..x.. 0.7003630 1.0000000
## Mechanical.reasoning.test..x.. 0.6374712 0.5907360
## Abstract.reasoning.test..x.. 0.6410886 0.1469074
## Mathematics.test..x.. 0.8525682 0.4126395
## Mechanical.reasoning.test..x..
## Sales.growth..x.. 0.7080738
## Sales.profitability..x.. 0.7461893
## New.account.sales..x.. 0.6374712
## Creativity.test..x.. 0.5907360
## Mechanical.reasoning.test..x.. 1.0000000
## Abstract.reasoning.test..x.. 0.3859502
## Mathematics.test..x.. 0.5745533
## Abstract.reasoning.test..x..
## Sales.growth..x.. 0.6744073
## Sales.profitability..x.. 0.4582909
## New.account.sales..x.. 0.6410886
## Creativity.test..x.. 0.1469074
## Mechanical.reasoning.test..x.. 0.3859502
## Abstract.reasoning.test..x.. 1.0000000
## Mathematics.test..x.. 0.5663721
## Mathematics.test..x..
## Sales.growth..x.. 0.9273116
## Sales.profitability..x.. 0.9423039
## New.account.sales..x.. 0.8525682
## Creativity.test..x.. 0.4126395
## Mechanical.reasoning.test..x.. 0.5745533
## Abstract.reasoning.test..x.. 0.5663721
## Mathematics.test..x.. 1.0000000ei <- eigen(cov_scale)
ei## eigen() decomposition
## $values
## [1] 5.02905754 0.93372873 0.50244444 0.42127696 0.08108383 0.02098245 0.01142605
##
## $vectors
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.4337984 0.112438563 0.0720594208 -0.044017623 0.63997595 0.2923924
## [2,] -0.4193550 -0.033225256 0.4494874948 0.008872357 -0.02063175 -0.7854288
## [3,] -0.4212930 -0.007982718 -0.2064606142 -0.323274107 -0.69824671 0.1527680
## [4,] -0.2943907 -0.666727051 -0.4569742750 -0.298224103 0.25772978 -0.1095087
## [5,] -0.3495270 -0.294369751 -0.0002311501 0.845895818 -0.16839065 0.2081046
## [6,] -0.2888688 0.644152902 -0.5981789463 0.160232312 0.07969614 -0.2338683
## [7,] -0.4076669 0.200196046 0.4283676615 -0.251646257 -0.03640218 0.4053100
## [,7]
## [1,] 0.54496226
## [2,] 0.05996414
## [3,] 0.40549942
## [4,] -0.30435053
## [5,] -0.06304471
## [6,] -0.23892166
## [7,] -0.61749754#Analisis Faktor ##Eksplorasi Data : Korelasi Antar Peubah
cor_sales <- cor(data_sales)
ggcorrplot(cor_sales, type = "lower", lab = TRUE)
Analisis Faktor
fa_sales1 <- fa(data_sales, nfactors = 7, fm = "minres", rotate = "none")
fa_sales1$Vaccounted## MR1 MR2 MR3 MR4 MR5
## SS loadings 4.9975546 0.8887708 0.47801327 0.28956747 0.067876978
## Proportion Var 0.7139364 0.1269673 0.06828761 0.04136678 0.009696711
## Cumulative Var 0.7139364 0.8409036 0.90919124 0.95055802 0.960254733
## Proportion Explained 0.7433879 0.1322050 0.07110464 0.04307326 0.010096723
## Cumulative Proportion 0.7433879 0.8755929 0.94669752 0.98977078 0.999867503
## MR6 MR7
## SS loadings 0.0008907373 7.000000e-30
## Proportion Var 0.0001272482 1.000000e-30
## Cumulative Var 0.9603819810 9.603820e-01
## Proportion Explained 0.0001324975 1.041252e-30
## Cumulative Proportion 1.0000000000 1.000000e+00SS_loadings <- fa_sales1$Vaccounted[1,]
number_of_factor <- seq_along(SS_loadings)
plot(number_of_factor, SS_loadings, type = "b", main = "Scree Plot", pch = 16)
Estimasi Factor Loading (2 Faktor)
fa_sales_2 <- fa(data_scale, nfactors = 2, fm = "minres", rotate = "none")## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefullyprint(fa_sales_2$loadings, cut = 0)##
## Loadings:
## MR1 MR2
## Sales.growth..x.. 0.987 -0.147
## Sales.profitability..x.. 0.933 -0.076
## New.account.sales..x.. 0.937 0.033
## Creativity.test..x.. 0.674 0.743
## Mechanical.reasoning.test..x.. 0.726 0.140
## Abstract.reasoning.test..x.. 0.583 -0.301
## Mathematics.test..x.. 0.911 -0.266
##
## MR1 MR2
## SS loadings 4.871 0.762
## Proportion Var 0.696 0.109
## Cumulative Var 0.696 0.805fa_sales_2$communalities## Sales.growth..x.. Sales.profitability..x..
## 0.9950000 0.8753413
## New.account.sales..x.. Creativity.test..x..
## 0.8787056 0.9950000
## Mechanical.reasoning.test..x.. Abstract.reasoning.test..x..
## 0.5460371 0.4306277
## Mathematics.test..x..
## 0.9006113Tanpa data data x5
fa_sales_21 <- fa(data_scale[,c(-5)], nfactors = 2, fm = "minres", rotate = "none")## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefullyfa_sales_21$communalities## Sales.growth..x.. Sales.profitability..x..
## 0.9879795 0.8378983
## New.account.sales..x.. Creativity.test..x..
## 0.9066160 0.9950000
## Abstract.reasoning.test..x.. Mathematics.test..x..
## 0.4274874 0.9226058fa_sales_21$communalities## Sales.growth..x.. Sales.profitability..x..
## 0.9879795 0.8378983
## New.account.sales..x.. Creativity.test..x..
## 0.9066160 0.9950000
## Abstract.reasoning.test..x.. Mathematics.test..x..
## 0.4274874 0.9226058Tanpa data data x6
fa_sales_22 <- fa(data_scale[,c(-6)], nfactors = 2, fm = "minres", rotate = "none")
fa_sales_22$communalities## Sales.growth..x.. Sales.profitability..x..
## 0.9416405 0.9484683
## New.account.sales..x.. Creativity.test..x..
## 0.8411659 0.9950000
## Mechanical.reasoning.test..x.. Mathematics.test..x..
## 0.5447689 0.9549137Tanpa data data x5 dan x6
fa_sales_23 <- fa(data_scale[,c(-5,-6)], nfactors = 2, fm = "minres", rotate = "none")
fa_sales_23$communalities## Sales.growth..x.. Sales.profitability..x.. New.account.sales..x..
## 0.9268832 0.9065354 0.8649337
## Creativity.test..x.. Mathematics.test..x..
## 0.9950000 0.9950000fa_sales_23 <- fa(data_sales[,c(-5,-6)], nfactors = 2, fm = "minres", rotate = "none")
fa_sales_23$communalities## Sales growth (x₁) Sales profitability (x₂) New-account sales (x₃)
## 0.9268832 0.9065354 0.8649337
## Creativity test (x₄) Mathematics test (x₇)
## 0.9950000 0.9950000Estimasi Factor Loading (2 Faktor) tanpa data data x5 dan x6
data_sales <- data_sales[,c(-5,-6)]
data_scale <- data_scale[,c(-5,-6)]fa_sales_2_ <- fa(data_sales, nfactors = 2, fm = "minres", rotate = "none")
print(fa_sales_2_$loadings, cut = 0)##
## Loadings:
## MR1 MR2
## Sales growth (x₁) 0.956 -0.117
## Sales profitability (x₂) 0.938 -0.161
## New-account sales (x₃) 0.926 0.082
## Creativity test (x₄) 0.691 0.719
## Mathematics test (x₇) 0.942 -0.329
##
## MR1 MR2
## SS loadings 4.017 0.672
## Proportion Var 0.803 0.134
## Cumulative Var 0.803 0.938fa_sales_2_ <- fa(data_scale, nfactors = 2, fm = "minres", rotate = "none")
print(fa_sales_2_$loadings, cut = 0)##
## Loadings:
## MR1 MR2
## Sales.growth..x.. 0.956 -0.117
## Sales.profitability..x.. 0.938 -0.161
## New.account.sales..x.. 0.926 0.082
## Creativity.test..x.. 0.691 0.719
## Mathematics.test..x.. 0.942 -0.329
##
## MR1 MR2
## SS loadings 4.017 0.672
## Proportion Var 0.803 0.134
## Cumulative Var 0.803 0.938Rotasi Varimax
fa_sales_2_vm <- fa(data_sales, nfactors = 2, fm = "minres", rotate = "varimax")
print(fa_sales_2_vm$loadings, cut = 0)##
## Loadings:
## MR1 MR2
## Sales growth (x₁) 0.893 0.359
## Sales profitability (x₂) 0.900 0.312
## New-account sales (x₃) 0.772 0.519
## Creativity test (x₄) 0.258 0.964
## Mathematics test (x₇) 0.984 0.166
##
## MR1 MR2
## SS loadings 3.237 1.452
## Proportion Var 0.647 0.290
## Cumulative Var 0.647 0.938Cut 0.6
print(fa_sales_2_vm$loadings, cut = 0.6)##
## Loadings:
## MR1 MR2
## Sales growth (x₁) 0.893
## Sales profitability (x₂) 0.900
## New-account sales (x₃) 0.772
## Creativity test (x₄) 0.964
## Mathematics test (x₇) 0.984
##
## MR1 MR2
## SS loadings 3.237 1.452
## Proportion Var 0.647 0.290
## Cumulative Var 0.647 0.938Diagram
fa.diagram(fa_sales_2_vm)
Mencari Skor Faktor dan RMS Overall
cov_sales1 <- cov(data_sales)
cov_sales1## Sales growth (x₁) Sales profitability (x₂)
## Sales growth (x₁) 53.83664 68.67786
## Sales profitability (x₂) 68.67786 103.06776
## New-account sales (x₃) 30.56453 40.17708
## Creativity test (x₄) 16.57967 21.59090
## Mathematics test (x₇) 71.69861 100.80882
## New-account sales (x₃) Creativity test (x₄)
## Sales growth (x₁) 30.56453 16.57967
## Sales profitability (x₂) 40.17708 21.59090
## New-account sales (x₃) 22.20500 13.03653
## Creativity test (x₄) 13.03653 15.60367
## Mathematics test (x₇) 42.33510 17.17633
## Mathematics test (x₇)
## Sales growth (x₁) 71.69861
## Sales profitability (x₂) 100.80882
## New-account sales (x₃) 42.33510
## Creativity test (x₄) 17.17633
## Mathematics test (x₇) 111.04327kor_sales1 <- cov2cor(cov_sales1)
kor_sales1## Sales growth (x₁) Sales profitability (x₂)
## Sales growth (x₁) 1.0000000 0.9219691
## Sales profitability (x₂) 0.9219691 1.0000000
## New-account sales (x₃) 0.8840023 0.8398304
## Creativity test (x₄) 0.5720363 0.5383886
## Mathematics test (x₇) 0.9273116 0.9423039
## New-account sales (x₃) Creativity test (x₄)
## Sales growth (x₁) 0.8840023 0.5720363
## Sales profitability (x₂) 0.8398304 0.5383886
## New-account sales (x₃) 1.0000000 0.7003630
## Creativity test (x₄) 0.7003630 1.0000000
## Mathematics test (x₇) 0.8525682 0.4126395
## Mathematics test (x₇)
## Sales growth (x₁) 0.9273116
## Sales profitability (x₂) 0.9423039
## New-account sales (x₃) 0.8525682
## Creativity test (x₄) 0.4126395
## Mathematics test (x₇) 1.0000000cov_scale1<- cov(data_scale)
ei1 <- eigen(cov_scale1)
ei1## eigen() decomposition
## $values
## [1] 4.08509751 0.68408040 0.12940390 0.07186530 0.02955288
##
## $vectors
## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.4774475 0.1509181 -0.06057376 0.85161497 0.1426549
## [2,] -0.4711487 0.2183976 -0.56043197 -0.42414431 0.4861471
## [3,] -0.4699552 -0.1290626 0.78481534 -0.23535590 0.3019229
## [4,] -0.3393199 -0.8713312 -0.25638340 -0.01311706 -0.2444167
## [5,] -0.4626737 0.3919835 0.02406778 -0.19821477 -0.7696841l1 <- sqrt(ei1$values[1]) * ei1$vectors[,1]
l2 <- sqrt(ei1$values[2]) * ei1$vectors[,2]
l <- cbind(l1,l2)
l## l1 l2
## [1,] -0.9649989 0.1248231
## [2,] -0.9522680 0.1806348
## [3,] -0.9498558 -0.1067466
## [4,] -0.6858207 -0.7206706
## [5,] -0.9351387 0.3242062t(l) %*% solve(kor_sales1)## Sales growth (x₁) Sales profitability (x₂) New-account sales (x₃)
## l1 -0.2362242 -0.2331078 -0.2325173
## l2 0.1824684 0.2640550 -0.1560439
## Creativity test (x₄) Mathematics test (x₇)
## l1 -0.1678835 -0.2289146
## l2 -1.0534882 0.4739300Skor Faktor
Skor_faktor <- t(t(l) %*% solve(kor_sales1) %*% t(data_scale))
colnames(Skor_faktor) <- c("Skor Faktor 1", "Skor Faktor 2")
Skor_faktor## Skor Faktor 1 Skor Faktor 2
## [1,] 0.984538774 -0.101452661
## [2,] 1.459155587 0.026492739
## [3,] 0.674363961 0.556931560
## [4,] -0.271379667 -0.652213976
## [5,] -0.135924172 0.530079785
## [6,] 0.721712355 -0.264199953
## [7,] 0.655858384 0.236887617
## [8,] -2.104961556 -0.568101369
## [9,] -0.190912021 0.491512840
## [10,] -0.854789322 0.260923870
## [11,] -0.358044484 0.047921450
## [12,] 0.007668608 0.616382035
## [13,] -0.787958286 -0.953057309
## [14,] 0.073468430 1.054608051
## [15,] -0.214245392 -0.243979968
## [16,] 1.722238242 -0.006249461
## [17,] -0.088616593 0.187575088
## [18,] -0.379003566 0.492108267
## [19,] 0.416094770 1.515957567
## [20,] -0.349754682 -1.798138200
## [21,] 1.310291467 -0.656907118
## [22,] -0.283196755 2.390380623
## [23,] 1.427554901 -0.442931805
## [24,] -0.691634704 0.388722718
## [25,] -1.303114450 -0.486292598
## [26,] 0.729442177 -0.069706582
## [27,] -1.058906709 0.020391799
## [28,] -1.028702043 1.671727967
## [29,] 1.135457311 -0.188567976
## [30,] -0.869645366 1.737060158
## [31,] -1.482387711 -0.953340465
## [32,] 1.305905258 -1.784980380
## [33,] 0.520047410 0.921827076
## [34,] 0.897138058 -0.459546235
## [35,] -1.522684198 -0.933861808
## [36,] -0.895825802 1.917814293
## [37,] 0.316052997 -2.714253670
## [38,] 0.033684783 -0.714734545
## [39,] -1.351091731 -0.046948612
## [40,] -1.013749439 -0.026990632
## [41,] -0.183437648 0.821732385
## [42,] 0.542200448 -0.926356211
## [43,] -0.784456657 -1.286675053
## [44,] 2.019227581 1.389405683
## [45,] 0.692833596 0.741226883
## [46,] -1.114154205 -1.490365778
## [47,] 1.337233669 0.091608735
## [48,] 1.861153022 -0.591191448
## [49,] -0.678604276 -0.371292801
## [50,] -0.846140353 0.623057426RMS Overall Sesuai EXCEl ASPRAK
r <- kor_sales1
ll <- l %*% t(l)
w <- matrix(0,ncol = 5, nrow = 5)
diag(w) <- diag(r)-diag(ll)
r## Sales growth (x₁) Sales profitability (x₂)
## Sales growth (x₁) 1.0000000 0.9219691
## Sales profitability (x₂) 0.9219691 1.0000000
## New-account sales (x₃) 0.8840023 0.8398304
## Creativity test (x₄) 0.5720363 0.5383886
## Mathematics test (x₇) 0.9273116 0.9423039
## New-account sales (x₃) Creativity test (x₄)
## Sales growth (x₁) 0.8840023 0.5720363
## Sales profitability (x₂) 0.8398304 0.5383886
## New-account sales (x₃) 1.0000000 0.7003630
## Creativity test (x₄) 0.7003630 1.0000000
## Mathematics test (x₇) 0.8525682 0.4126395
## Mathematics test (x₇)
## Sales growth (x₁) 0.9273116
## Sales profitability (x₂) 0.9423039
## New-account sales (x₃) 0.8525682
## Creativity test (x₄) 0.4126395
## Mathematics test (x₇) 1.0000000ll## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9468036 0.9414850 0.9032853 0.5718599 0.9428762
## [2,] 0.9414850 0.9394434 0.8852351 0.5229069 0.9490656
## [3,] 0.9032853 0.8852351 0.9136208 0.7283599 0.8536389
## [4,] 0.5718599 0.5229069 0.7283599 0.9897161 0.4076915
## [5,] 0.9428762 0.9490656 0.8536389 0.4076915 0.9795940w## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.05319639 0.00000000 0.00000000 0.00000000 0.00000000
## [2,] 0.00000000 0.06055664 0.00000000 0.00000000 0.00000000
## [3,] 0.00000000 0.00000000 0.08637916 0.00000000 0.00000000
## [4,] 0.00000000 0.00000000 0.00000000 0.01028387 0.00000000
## [5,] 0.00000000 0.00000000 0.00000000 0.00000000 0.02040601res <- r-(ll+w)
res## Sales growth (x₁) Sales profitability (x₂)
## Sales growth (x₁) 0.0000000000 -0.019515830
## Sales profitability (x₂) -0.0195158300 0.000000000
## New-account sales (x₃) -0.0192830434 -0.045404760
## Creativity test (x₄) 0.0001764445 0.015481737
## Mathematics test (x₇) -0.0155645922 -0.006761697
## New-account sales (x₃) Creativity test (x₄)
## Sales growth (x₁) -0.019283043 0.0001764445
## Sales profitability (x₂) -0.045404760 0.0154817373
## New-account sales (x₃) 0.000000000 -0.0279968217
## Creativity test (x₄) -0.027996822 0.0000000000
## Mathematics test (x₇) -0.001070786 0.0049479475
## Mathematics test (x₇)
## Sales growth (x₁) -0.015564592
## Sales profitability (x₂) -0.006761697
## New-account sales (x₃) -0.001070786
## Creativity test (x₄) 0.004947947
## Mathematics test (x₇) 0.000000000res^2## Sales growth (x₁) Sales profitability (x₂)
## Sales growth (x₁) 0.000000e+00 3.808676e-04
## Sales profitability (x₂) 3.808676e-04 0.000000e+00
## New-account sales (x₃) 3.718358e-04 2.061592e-03
## Creativity test (x₄) 3.113265e-08 2.396842e-04
## Mathematics test (x₇) 2.422565e-04 4.572055e-05
## New-account sales (x₃) Creativity test (x₄)
## Sales growth (x₁) 3.718358e-04 3.113265e-08
## Sales profitability (x₂) 2.061592e-03 2.396842e-04
## New-account sales (x₃) 0.000000e+00 7.838220e-04
## Creativity test (x₄) 7.838220e-04 0.000000e+00
## Mathematics test (x₇) 1.146582e-06 2.448218e-05
## Mathematics test (x₇)
## Sales growth (x₁) 2.422565e-04
## Sales profitability (x₂) 4.572055e-05
## New-account sales (x₃) 1.146582e-06
## Creativity test (x₄) 2.448218e-05
## Mathematics test (x₇) 0.000000e+00sum(res^2)## [1] 0.008302878sum(w)## [1] 0.2308221ressumsq <- sum(res^2)
ressumsq## [1] 0.008302878RMS Overall
rms_overall1 <- sqrt((1/(5*(5-1)))*ressumsq)
rms_overall1## [1] 0.02037508Nilai RMS overall = 0.02037508 yang lebih kecil dari 0.05 mengindikasikan bahwa model diperoleh cukup baik.