Y: Kelulusan Ujian Akhir
X1: Jam belajar
X2: Kesulitan
soal
X3: Tingkat stres
X4: Dukungan orang tua
X1 : Jam Belajar (hour)
Membangkitkan variabel X1 dengan rentang
waktu belajar minimal 0 jam hingga maksimal 12 jam dan banyaknya data
pelajar adalah 100
min_hours <- 0
max_hours <- 12
set.seed(100)
x1 <- runif(100, min = min_hours, max = max_hours)
x1## [1] 3.6931933 3.0920700 6.6278692 0.6765978 5.6225914 5.8052488
## [7] 9.7488314 4.4438464 6.5587031 2.0431446 7.4999577 10.5859862
## [13] 3.3642461 4.7818548 9.1506130 8.0282605 2.4553459 4.2902982
## [19] 4.3137014 8.2834863 6.4297338 8.5296461 6.4601844 8.9876667
## [25] 5.0412174 2.0570426 9.2436193 10.5834431 6.5891605 3.3326851
## [31] 5.8596719 11.1420609 4.1843038 11.4498925 8.3432897 10.6734425
## [37] 2.1648869 7.5526902 11.8747696 1.5634664 3.9679263 10.3814466
## [43] 9.3310133 9.9276414 7.2398923 5.8947819 9.3643021 10.6107243
## [49] 2.4925668 3.6850308 3.9663582 2.3841488 2.8283316 3.2986399
## [55] 7.0958526 3.0406878 1.4818468 2.7588706 7.1709035 2.5369027
## [61] 5.5644141 7.7652143 11.5268771 8.1167781 5.3417763 4.2932854
## [67] 5.4687775 5.3449677 2.9411111 8.3322085 4.9468444 3.9327104
## [73] 6.8707772 11.6039890 7.9413483 7.4963726 10.2798365 9.2973467
## [79] 10.0083252 1.0981233 5.5143058 7.1927779 11.0366629 11.7938889
## [85] 0.4536310 6.9352488 8.7997700 2.9849088 3.6088383 8.8016004
## [91] 10.8834525 2.5178012 4.2976559 5.3795897 10.8771172 4.6732715
## [97] 6.2095170 1.5028691 0.3617489 9.2616659X2 : Kesulitan Soal
Membangkitkan variabel x2 dengan kesulitan
soal 1 (Mudah) hingga 10 (Sulit) dengan 100 data
set.seed(100)
min_difficulty <- 1
max_difficulty <- 10
x2 <- runif(100, min = min_difficulty, max = max_difficulty)
x2## [1] 3.769895 3.319053 5.970902 1.507448 5.216944 5.353937 8.311624 4.332885
## [9] 5.919027 2.532358 6.624968 8.939490 3.523185 4.586391 7.862960 7.021195
## [17] 2.841509 4.217724 4.235276 7.212615 5.822300 7.397235 5.845138 7.740750
## [25] 4.780913 2.542782 7.932714 8.937582 5.941870 3.499514 5.394754 9.356546
## [33] 4.138228 9.587419 7.257467 9.005082 2.623665 6.664518 9.906077 2.172600
## [41] 3.975945 8.786085 7.998260 8.445731 6.429919 5.421086 8.023227 8.958043
## [49] 2.869425 3.763773 3.974769 2.788112 3.121249 3.473980 6.321889 3.280516
## [57] 2.111385 3.069153 6.378178 2.902677 5.173311 6.823911 9.645158 7.087584
## [65] 5.006332 4.219964 5.101583 5.008726 3.205833 7.249156 4.710133 3.949533
## [73] 6.153083 9.702992 6.956011 6.622279 8.709877 7.973010 8.506244 1.823593
## [81] 5.135729 6.394583 9.277497 9.845417 1.340223 6.201437 7.599828 3.238682
## [89] 3.706629 7.601200 9.162589 2.888351 4.223242 5.034692 9.157838 4.504954
## [97] 5.657138 2.127152 1.271312 7.946249x3 : Tingkat Stress
Membangkitkan variabel X3 yakni Tingkatan
Stress dengan rentang 0 (Tidak Stress) hingga 1 (Stress)
set.seed(100)
n <- 100
x3 <- round(runif(n))
x3## [1] 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1 1 0 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 0
## [38] 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0 0 1 1
## [75] 1 1 1 1 1 0 0 1 1 1 0 1 1 0 0 1 1 0 0 0 1 0 1 0 0 1X4 : Dukungan Ortu
Membangkitkan variabel X4 yakni Dukungan
Orang Tua dengan rentang 0 (Tidak Didukung); 1 (Di Dukung); 2 (Sangat
Didukung)
set.seed(100)
dukung_ortu <- c(0, 1, 2)
dukung_probs <- c(0.2,0.3, 0.5)
x4 <- sample(dukung_ortu, 100, replace = TRUE, prob = dukung_probs)
x4## [1] 2 2 1 2 2 2 0 2 1 2 1 0 2 2 1 1 2 2 2 1 1 1 1 1 2 2 1 0 1 2 2 0 2 0 1 0 2
## [38] 1 0 2 2 0 1 0 1 2 1 0 2 2 2 2 2 2 1 2 2 2 1 2 2 1 0 1 2 2 2 2 2 1 2 2 1 0
## [75] 1 1 0 1 0 2 2 1 0 0 2 1 1 2 2 1 0 2 2 2 0 2 1 2 2 1Menentukan Koefesien Regresi
b0 <- -3.9
b1 <- 1
b2 <- -0.4
b3 <- -0.5
b4 <- 0.7set.seed(2)
datasup <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datasup## [1] -0.31476467 -0.73555099 0.53950844 -2.42638154 1.03581398 1.16367417
## [7] 2.02418199 0.21069251 0.49109220 -1.46979877 1.14997041 2.61019035
## [13] -0.54502774 0.44729837 2.30542909 1.51978238 -1.18125784 0.10320877
## [19] 0.11959096 1.69844044 0.40081369 1.87075230 0.42212907 2.19136671
## [25] 0.62885218 -1.46007021 2.37053352 2.60841014 0.51241237 -0.56712044
## [31] 1.20177035 2.99944263 0.02901265 3.21492474 1.74030277 2.67140973
## [37] -1.38457914 1.18688314 3.51233875 -1.80557349 -0.12245158 2.46701259
## [43] 2.43170929 2.14934898 0.96792459 1.22634730 2.45501149 2.62750703
## [49] -1.15520326 -0.32047847 -0.12354928 -1.23109581 -0.92016786 -0.59095205
## [55] 0.86709685 -0.77151851 -1.86270726 -0.96879055 0.91963245 -1.12416813
## [61] 0.99508990 1.33565003 3.26881397 1.58174464 0.83924338 0.10529978
## [67] 0.92814423 0.84147741 -0.84122225 1.73254598 0.56279109 -0.14710270
## [73] 0.70954404 3.32279231 1.45894379 1.14746082 2.39588556 2.40814266
## [79] 2.20582763 -2.13131366 0.96001409 0.93494451 2.92566405 3.45572225
## [85] -2.58245833 0.75467417 2.05983901 -0.81056382 -0.37381319 2.06112029
## [91] 2.81841675 -1.13753915 0.10835911 0.86571281 2.81398204 0.37129008
## [97] 0.24666190 -1.84799166 -2.64677574 2.38316615p <- exp(datasup)/(1+exp(datasup))
p## [1] 0.42195217 0.32397778 0.63169806 0.08118297 0.73804150 0.76199969
## [7] 0.88331274 0.55247914 0.62036369 0.18697320 0.75950551 0.93151454
## [13] 0.36701878 0.60999671 0.90932568 0.82050643 0.23482611 0.52577931
## [19] 0.52986216 0.84533094 0.59888314 0.86654530 0.60399260 0.89947155
## [25] 0.65222915 0.18845659 0.91455256 0.93140088 0.62537182 0.36190153
## [31] 0.76883957 0.95254894 0.50725265 0.96139207 0.85072552 0.93531837
## [37] 0.20027458 0.76618315 0.97103681 0.14117396 0.46942530 0.92179668
## [43] 0.91921356 0.89560793 0.72470563 0.77317863 0.92092716 0.93261104
## [49] 0.23953997 0.42055915 0.46915191 0.22598969 0.28492369 0.35641644
## [55] 0.70414125 0.31615071 0.13438781 0.27512164 0.71496721 0.24523896
## [61] 0.73009210 0.79177368 0.96334331 0.82945146 0.69830584 0.52630065
## [67] 0.71669864 0.69877628 0.30127743 0.84973779 0.63709810 0.46329050
## [73] 0.67030040 0.96520250 0.81137108 0.75904682 0.91651302 0.91744612
## [79] 0.90077161 0.10609035 0.72312463 0.71807734 0.94910062 0.96940133
## [85] 0.07027594 0.68019632 0.88693803 0.30777036 0.40761994 0.88706645
## [91] 0.94366296 0.24277246 0.52706330 0.70385284 0.94342673 0.59177067
## [97] 0.56135471 0.13610887 0.06618801 0.91553460set.seed(3)
y <- rbinom(n,1,p)
y## [1] 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 0 0 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 0 0 1 1 1 1 1 1 1 0 1 0 0 1 1 0 1 0 0 1 0 0 1 1 1 0 1 0 1 0 1 0 1 0 0
## [75] 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 0 1 0 1 0 1 1 1 0 0 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 0 3.6931933 3.769895 0 2
## 2 1 3.0920700 3.319053 0 2
## 3 1 6.6278692 5.970902 1 1
## 4 0 0.6765978 1.507448 0 2
## 5 1 5.6225914 5.216944 0 2
## 6 1 5.8052488 5.353937 0 2
## 7 1 9.7488314 8.311624 1 0
## 8 1 4.4438464 4.332885 0 2
## 9 1 6.5587031 5.919027 1 1
## 10 0 2.0431446 2.532358 0 2
## 11 1 7.4999577 6.624968 1 1
## 12 1 10.5859862 8.939490 1 0
## 13 0 3.3642461 3.523185 0 2
## 14 1 4.7818548 4.586391 0 2
## 15 1 9.1506130 7.862960 1 1
## 16 0 8.0282605 7.021195 1 1
## 17 0 2.4553459 2.841509 0 2
## 18 0 4.2902982 4.217724 0 2
## 19 0 4.3137014 4.235276 0 2
## 20 1 8.2834863 7.212615 1 1
## 21 1 6.4297338 5.822300 1 1
## 22 1 8.5296461 7.397235 1 1
## 23 1 6.4601844 5.845138 1 1
## 24 1 8.9876667 7.740750 1 1
## 25 1 5.0412174 4.780913 0 2
## 26 0 2.0570426 2.542782 0 2
## 27 1 9.2436193 7.932714 1 1
## 28 1 10.5834431 8.937582 1 0
## 29 1 6.5891605 5.941870 1 1
## 30 1 3.3326851 3.499514 0 2
## 31 1 5.8596719 5.394754 0 2
## 32 1 11.1420609 9.356546 1 0
## 33 1 4.1843038 4.138228 0 2
## 34 1 11.4498925 9.587419 1 0
## 35 1 8.3432897 7.257467 1 1
## 36 1 10.6734425 9.005082 1 0
## 37 1 2.1648869 2.623665 0 2
## 38 1 7.5526902 6.664518 1 1
## 39 1 11.8747696 9.906077 1 0
## 40 0 1.5634664 2.172600 0 2
## 41 0 3.9679263 3.975945 0 2
## 42 1 10.3814466 8.786085 1 0
## 43 1 9.3310133 7.998260 1 1
## 44 1 9.9276414 8.445731 1 0
## 45 1 7.2398923 6.429919 1 1
## 46 1 5.8947819 5.421086 0 2
## 47 1 9.3643021 8.023227 1 1
## 48 1 10.6107243 8.958043 1 0
## 49 0 2.4925668 2.869425 0 2
## 50 1 3.6850308 3.763773 0 2
## 51 0 3.9663582 3.974769 0 2
## 52 0 2.3841488 2.788112 0 2
## 53 1 2.8283316 3.121249 0 2
## 54 1 3.2986399 3.473980 0 2
## 55 0 7.0958526 6.321889 1 1
## 56 1 3.0406878 3.280516 0 2
## 57 0 1.4818468 2.111385 0 2
## 58 0 2.7588706 3.069153 0 2
## 59 1 7.1709035 6.378178 1 1
## 60 0 2.5369027 2.902677 0 2
## 61 0 5.5644141 5.173311 0 2
## 62 1 7.7652143 6.823911 1 1
## 63 1 11.5268771 9.645158 1 0
## 64 1 8.1167781 7.087584 1 1
## 65 0 5.3417763 5.006332 0 2
## 66 1 4.2932854 4.219964 0 2
## 67 0 5.4687775 5.101583 0 2
## 68 1 5.3449677 5.008726 0 2
## 69 0 2.9411111 3.205833 0 2
## 70 1 8.3322085 7.249156 1 1
## 71 0 4.9468444 4.710133 0 2
## 72 1 3.9327104 3.949533 0 2
## 73 0 6.8707772 6.153083 1 1
## 74 0 11.6039890 9.702992 1 0
## 75 1 7.9413483 6.956011 1 1
## 76 1 7.4963726 6.622279 1 1
## 77 1 10.2798365 8.709877 1 0
## 78 1 9.2973467 7.973010 1 1
## 79 1 10.0083252 8.506244 1 0
## 80 0 1.0981233 1.823593 0 2
## 81 0 5.5143058 5.135729 0 2
## 82 1 7.1927779 6.394583 1 1
## 83 1 11.0366629 9.277497 1 0
## 84 1 11.7938889 9.845417 1 0
## 85 1 0.4536310 1.340223 0 2
## 86 0 6.9352488 6.201437 1 1
## 87 1 8.7997700 7.599828 1 1
## 88 0 2.9849088 3.238682 0 2
## 89 1 3.6088383 3.706629 0 2
## 90 0 8.8016004 7.601200 1 1
## 91 1 10.8834525 9.162589 1 0
## 92 0 2.5178012 2.888351 0 2
## 93 1 4.2976559 4.223242 0 2
## 94 0 5.3795897 5.034692 0 2
## 95 1 10.8771172 9.157838 1 0
## 96 1 4.6732715 4.504954 0 2
## 97 1 6.2095170 5.657138 1 1
## 98 0 1.5028691 2.127152 0 2
## 99 0 0.3617489 1.271312 0 2
## 100 1 9.2616659 7.946249 1 1modelreglog <- glm(y~x1+x2+x3+x4, family = binomial(link = "logit"), data=datagab)summary(modelreglog)##
## Call:
## glm(formula = y ~ x1 + x2 + x3 + x4, family = binomial(link = "logit"),
## data = datagab)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.90751 2.99031 -0.638 0.5235
## x1 0.40853 0.19276 2.119 0.0341 *
## x2 NA NA NA NA
## x3 0.34385 1.46382 0.235 0.8143
## x4 0.09566 1.28963 0.074 0.9409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 128.207 on 99 degrees of freedom
## Residual deviance: 99.134 on 96 degrees of freedom
## AIC: 107.13
##
## Number of Fisher Scoring iterations: 5Model regresi ini memperkirakan tentang kelulusan siswa di Ujian
Akhir dengan 4 parameter, yakni lama belajar, tingkat kesulitan soal,
tingkat stress siswa, dan dukungan orang tua. Variabel yang paling
berpengaruh untuk kelulusan disini adalah lama belajar dan dukungan
orang tua. Semakin lama waktu belajar dan semakin banyak dukungan ortu
akan mempengaruhi kelulusan. Namun, kesulitan soal dan tingkat stress
juga akan berpengaruh. Semakin sulit soal dan semakin tinggi tingkat
stress akan menggugurkan siswa di ujian akhir.
Nilai akhir (Y) yang
menunjukkan nilai 0 berarti siswa tidak lulus ujian akhir, sedangkan
nilai 1 berarti siswa lulus ujian.