R-Squared
Riyo
2025-02-12
#membuat dataset
jam_belajar <- c(1,2,3,4,5)
skor_ujian <- c(50,55,65,70,80)
#model regresi linear
#lm=linear model
#lm(y ~ x)
#modeljb adalah nama model dari "model jam belajar"
modeljb <- lm(skor_ujian ~ jam_belajar)
#menampilkan ringkasan mode
summary(modeljb)##
## Call:
## lm(formula = skor_ujian ~ jam_belajar)
##
## Residuals:
## 1 2 3 4 5
## 1.0 -1.5 1.0 -1.5 1.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.500 1.658 25.02 0.000140 ***
## jam_belajar 7.500 0.500 15.00 0.000643 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.581 on 3 degrees of freedom
## Multiple R-squared: 0.9868, Adjusted R-squared: 0.9825
## F-statistic: 225 on 1 and 3 DF, p-value: 0.0006431#Menampilkan nilai R-Squared, pakai $ kalau mau mengambil variabel/bagian tertentu
r_squared <- summary(modeljb)$r.squared
print(paste("Nilai R-Squared:", r_squared))## [1] "Nilai R-Squared: 0.986842105263158"#selang kepercayaan (confident interval)
confint(modeljb, level=0.95)## 2.5 % 97.5 %
## (Intercept) 36.222510 46.777490
## jam_belajar 5.908777 9.091223Latihan 2
jam_lembur <- c(10,15,8,4,6)
gaji <- c(3200000,3400000,2900000,2700000,2850000)
model <- lm(gaji ~ jam_lembur)
summary(model)##
## Call:
## lm(formula = gaji ~ jam_lembur)
##
## Residuals:
## 1 2 3 4 5
## 98174 -29775 -70646 -8287 10534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2445927 80504 30.383 7.83e-05 ***
## jam_lembur 65590 8572 7.652 0.00464 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 72330 on 3 degrees of freedom
## Multiple R-squared: 0.9513, Adjusted R-squared: 0.935
## F-statistic: 58.55 on 1 and 3 DF, p-value: 0.004636r_squared <- summary(model)$r.squared
print(paste("Nilai R-Squared:", r_squared))## [1] "Nilai R-Squared: 0.951257066089748"confint(model, level=0.95)## 2.5 % 97.5 %
## (Intercept) 2189727.3 2702126.61
## jam_lembur 38309.9 92869.88Latihan 3
data(cars)
#prompt "head" untuk menampilkan 6 data teratas, digunakan untuk melihat nama variabelnya
head(cars)## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10modelcars <- lm(cars$dist ~ cars$speed)
#perlu dikasih "data=cars" supaya tersambung dengan dataset. Jika tidak, maka variabel "speed" dan "dist" tidak dapat terbaca
summary(modelcars)##
## Call:
## lm(formula = cars$dist ~ cars$speed)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.069 -9.525 -2.272 9.215 43.201
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.5791 6.7584 -2.601 0.0123 *
## cars$speed 3.9324 0.4155 9.464 1.49e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.38 on 48 degrees of freedom
## Multiple R-squared: 0.6511, Adjusted R-squared: 0.6438
## F-statistic: 89.57 on 1 and 48 DF, p-value: 1.49e-12r_squared <- summary(modelcars)$r.squared
print(paste("Nilai R-Squared:", r_squared))## [1] "Nilai R-Squared: 0.651079380758251"confint(modelcars, level=0.95)## 2.5 % 97.5 %
## (Intercept) -31.167850 -3.990340
## cars$speed 3.096964 4.767853#plot(x,y), "main" untuk judul, "xlab" dab "ylab" untuk nama di garis x dan y, "pch (plot character" itu jenis titik, "col" itu warna, "abline" itu garis, "lwd" itu ketebalan garis
plot(cars$speed, cars$dist, main = "Regresi Linier: Speed vs Distance",
xlab = "Kecepatan (speed)", ylab = "Jarak Berhenti (dist)", pch = 19, col = "blue")
abline(modelcars, col ="red", lwd = 2)
#uji korelasi
cor.test(cars$speed, cars$dist)##
## Pearson's product-moment correlation
##
## data: cars$speed and cars$dist
## t = 9.464, df = 48, p-value = 1.49e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6816422 0.8862036
## sample estimates:
## cor
## 0.8068949#Regresi linear berganda
pengalaman <- c(2, 3, 5, 7, 9)
pendidikan <- c(12, 14, 16, 18, 20)
gaji <- c(5, 6, 9, 11, 13)
#buat data frame
data <- data.frame(Pengalaman = pengalaman, Pendidikan = pendidikan, Gaji = gaji)
data## Pengalaman Pendidikan Gaji
## 1 2 12 5
## 2 3 14 6
## 3 5 16 9
## 4 7 18 11
## 5 9 20 13#model regresi
modelpp <- lm(Gaji ~ Pengalaman + Pendidikan, data = data)
#Output hasil regresi
summary(modelpp)##
## Call:
## lm(formula = Gaji ~ Pengalaman + Pendidikan, data = data)
##
## Residuals:
## 1 2 3 4 5
## -5.551e-17 -3.000e-01 4.000e-01 1.000e-01 -2.000e-01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2000 5.7210 0.210 0.853
## Pengalaman 1.0000 0.6124 1.633 0.244
## Pendidikan 0.1500 0.5545 0.271 0.812
##
## Residual standard error: 0.3873 on 2 degrees of freedom
## Multiple R-squared: 0.9933, Adjusted R-squared: 0.9866
## F-statistic: 148.3 on 2 and 2 DF, p-value: 0.006696#cara kerja data dalam jumlah besar
#import dataset
df <- read.csv("data_regresi_linier.csv")
df## Pengalaman Pendidikan Umur Jam_Kerja Gaji
## 1 7 11 43 20 49.90679
## 2 20 11 50 49 73.00960
## 3 29 16 25 22 71.56856
## 4 15 15 21 33 54.66767
## 5 11 12 39 49 58.17922
## 6 8 18 47 23 65.22044
## 7 29 19 30 37 80.04735
## 8 21 15 23 56 58.96196
## 9 7 19 34 44 63.58700
## 10 26 19 25 20 67.60586
## 11 19 15 49 34 68.41218
## 12 23 10 57 28 76.17853
## 13 11 13 21 34 46.34972
## 14 11 19 34 43 64.12788
## 15 24 15 30 23 60.23464
## 16 21 15 27 31 59.67790
## 17 4 14 45 56 55.81862
## 18 8 10 24 56 41.99975
## 19 24 17 25 25 69.57253
## 20 3 14 45 58 56.86519
## 21 22 14 23 43 55.84304
## 22 21 16 38 30 55.23305
## 23 2 13 39 32 45.10519
## 24 24 15 52 53 74.48455
## 25 12 13 39 44 68.11008
## 26 6 12 31 26 49.54909
## 27 2 16 20 23 36.25925
## 28 28 17 45 27 86.66218
## 29 21 13 33 39 67.50883
## 30 1 11 57 48 57.27584
## 31 12 19 56 29 58.60297
## 32 26 12 30 53 79.55471
## 33 22 10 55 45 65.65214
## 34 29 17 32 22 59.97301
## 35 12 12 22 25 49.61285
## 36 25 19 52 24 90.19876
## 37 17 16 25 24 61.42136
## 38 27 19 29 42 76.31955
## 39 27 14 24 28 63.77377
## 40 10 19 42 54 74.26708
## 41 28 14 29 39 71.19464
## 42 28 16 21 54 71.70596
## 43 16 18 32 37 64.02101
## 44 15 14 59 36 70.78703
## 45 15 10 21 43 45.63018
## 46 19 19 39 22 64.69662
## 47 12 19 20 51 51.23522
## 48 23 10 56 54 76.10830
## 49 20 11 28 57 57.35704
## 50 25 15 36 32 62.08205
## 51 3 18 28 44 54.09947
## 52 5 17 30 28 56.76001
## 53 19 14 34 23 62.49158
## 54 7 10 43 33 43.14750
## 55 21 16 57 56 78.35186
## 56 9 14 54 53 73.60386
## 57 7 15 49 30 54.18756
## 58 18 16 50 22 66.07683
## 59 4 12 24 47 48.45026
## 60 25 19 33 48 70.85828
## 61 28 12 30 28 62.72688
## 62 14 14 28 22 43.92987
## 63 18 15 53 44 71.09316
## 64 26 18 31 53 77.39891
## 65 9 14 54 31 54.33503
## 66 26 10 54 28 72.40237
## 67 21 13 20 34 54.50181
## 68 2 14 59 59 70.36245
## 69 20 19 41 44 75.61957
## 70 28 19 48 24 85.22070
## 71 15 14 27 30 53.92612
## 72 28 16 30 53 72.35297
## 73 7 13 56 43 64.71149
## 74 12 10 33 34 53.01649
## 75 29 14 49 57 83.84430
## 76 8 16 54 27 62.90296
## 77 15 19 40 24 60.49004
## 78 3 19 56 45 65.38550
## 79 14 15 24 37 47.28697
## 80 17 14 38 25 67.64788
## 81 4 13 33 56 47.88068
## 82 18 11 45 32 58.23547
## 83 8 13 23 47 46.00796
## 84 4 19 44 24 61.67462
## 85 2 19 44 32 56.52525
## 86 6 12 37 36 56.73871
## 87 22 19 59 59 86.23255
## 88 10 10 27 51 52.19497
## 89 4 17 58 48 64.82326
## 90 22 14 59 22 83.22575
## 91 29 13 33 59 69.06201
## 92 18 17 51 53 79.74910
## 93 26 16 57 59 91.36888
## 94 12 11 52 46 66.66900
## 95 2 10 42 32 45.27356
## 96 10 13 34 52 57.55090
## 97 4 17 52 25 59.31906
## 98 14 11 44 43 60.56499
## 99 16 12 36 38 63.41940
## 100 15 10 52 35 73.55262
## 101 8 10 21 59 45.81254
## 102 14 12 33 47 51.35099
## 103 23 14 59 52 81.85859
## 104 28 12 59 32 90.79855
## 105 25 10 58 44 72.18163
## 106 8 10 25 55 57.59558
## 107 21 17 25 46 73.88850
## 108 16 19 22 57 62.93206
## 109 13 11 26 26 49.24492
## 110 18 12 27 24 59.64198
## 111 15 11 34 50 57.30436
## 112 21 12 48 42 68.78210
## 113 24 16 52 29 72.19160
## 114 26 10 49 51 69.52622
## 115 25 19 58 49 85.25361
## 116 28 17 46 58 79.08030
## 117 28 19 55 50 89.87042
## 118 28 19 48 37 78.45013
## 119 13 19 57 55 81.36893
## 120 9 11 52 21 46.92052
## 121 29 12 56 33 79.15440
## 122 15 18 46 32 72.36764
## 123 13 16 52 48 62.03132
## 124 1 13 23 31 41.07028
## 125 25 19 41 58 88.83834
## 126 7 14 21 21 35.15179
## 127 9 11 29 51 60.56286
## 128 24 17 24 32 65.29510
## 129 1 13 29 46 43.17494
## 130 12 18 52 36 69.72115
## 131 8 14 57 59 74.19163
## 132 24 18 32 50 74.18003
## 133 11 13 50 53 69.99378
## 134 19 19 55 43 75.79663
## 135 17 14 43 54 70.94680
## 136 8 18 34 25 47.43143
## 137 3 17 48 38 59.64387
## 138 3 12 27 49 42.48046
## 139 1 10 24 26 30.92235
## 140 27 12 48 29 76.88047
## 141 5 13 23 51 53.85655
## 142 10 11 31 53 57.64569
## 143 7 10 21 47 38.73844
## 144 26 16 46 34 73.61796
## 145 9 17 50 49 75.36549
## 146 28 16 55 56 88.60858
## 147 7 14 55 48 77.89665
## 148 9 10 45 41 57.96659
## 149 8 16 46 23 58.46025
## 150 12 16 24 39 52.95442
## 151 2 18 39 51 60.66770
## 152 1 12 30 55 47.93765
## 153 16 18 29 23 61.34054
## 154 23 10 59 46 80.48351
## 155 23 10 57 36 67.77005
## 156 24 13 25 31 51.94384
## 157 5 18 27 41 44.21897
## 158 3 15 42 36 56.73996
## 159 12 12 45 29 51.68317
## 160 8 10 31 20 31.13555
## 161 22 13 45 41 71.94289
## 162 27 18 32 45 87.16500
## 163 3 12 59 37 59.36965
## 164 1 18 37 33 56.99245
## 165 3 16 44 48 60.40915
## 166 5 13 52 31 56.40555
## 167 15 12 59 55 78.99538
## 168 14 19 31 54 66.24863
## 169 3 14 55 31 59.33696
## 170 1 14 23 26 40.50849
## 171 5 12 24 48 43.45955
## 172 26 18 56 30 86.36021
## 173 23 13 27 52 64.22520
## 174 14 14 47 25 57.22127
## 175 7 13 50 34 61.08255
## 176 27 14 28 51 65.20766
## 177 9 16 48 30 61.54091
## 178 15 18 33 42 64.65441
## 179 15 16 59 45 81.62948
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## 182 28 19 42 48 86.68883
## 183 13 16 20 23 55.45104
## 184 19 19 56 36 81.58905
## 185 7 14 40 54 60.65176
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## 201 19 14 57 47 84.25375
## 202 22 14 21 48 67.39381
## 203 28 15 26 40 66.33279
## 204 2 12 37 35 54.45363
## 205 10 17 46 56 70.94334
## 206 13 10 53 39 60.91457
## 207 25 15 46 25 67.43299
## 208 21 13 36 38 62.28765
## 209 6 10 43 24 45.55912
## 210 28 16 44 22 73.05208
## 211 28 18 26 20 65.80287
## 212 12 13 25 57 56.81523
## 213 12 13 43 34 61.08469
## 214 20 15 52 53 83.85323
## 215 11 12 48 54 70.14777
## 216 26 15 41 44 67.07551
## 217 23 16 45 43 62.23039
## 218 28 19 47 31 84.67160
## 219 25 19 40 37 69.06561
## 220 7 12 26 34 44.07617
## 221 1 16 36 35 44.64943
## 222 1 12 39 46 41.49010
## 223 25 11 39 27 65.50731
## 224 27 19 41 44 83.89578
## 225 25 13 47 36 69.01745
## 226 20 17 59 48 69.34479
## 227 13 18 26 27 52.36878
## 228 9 16 20 34 50.55902
## 229 3 10 51 46 48.30544
## 230 7 12 32 26 46.71680
## 231 6 18 49 22 59.42345
## 232 8 10 42 48 58.69646
## 233 27 18 38 43 77.69790
## 234 9 17 51 22 56.83466
## 235 5 10 49 39 60.18286
## 236 1 15 48 34 54.20735
## 237 19 14 48 44 71.06348
## 238 10 15 49 49 62.53366
## 239 12 19 35 33 59.58359
## 240 24 14 59 26 70.87599
## 241 15 15 38 36 60.93527
## 242 27 14 37 39 79.15854
## 243 22 14 20 55 56.04049
## 244 24 13 33 28 53.63219
## 245 9 12 21 57 58.55585
## 246 20 12 47 41 67.15338
## 247 17 13 49 21 58.65464
## 248 17 18 57 43 83.79952
## 249 26 11 23 27 52.57192
## 250 20 18 20 30 67.73005
## 251 12 10 27 32 50.08963
## 252 7 10 48 36 51.96044
## 253 2 14 58 27 68.28654
## 254 3 15 22 40 42.37143
## 255 17 15 51 37 73.91048
## 256 5 12 29 49 51.09646
## 257 17 16 29 37 66.90871
## 258 24 18 38 50 84.60756
## 259 17 19 53 57 82.16002
## 260 27 17 52 51 81.53291
## 261 17 15 42 30 65.89986
## 262 2 17 47 44 57.31142
## 263 2 14 51 52 71.56694
## 264 28 17 26 57 75.58268
## 265 22 19 48 27 72.67339
## 266 23 13 27 30 59.42340
## 267 5 19 20 41 44.36437
## 268 1 17 22 43 42.24193
## 269 1 19 43 36 63.90207
## 270 19 11 42 31 57.41553
## 271 2 14 27 51 43.96545
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## 273 12 13 22 35 54.62852
## 274 26 15 52 41 82.69477
## 275 6 10 47 47 61.38024
## 276 23 18 27 39 63.41602
## 277 4 10 53 52 52.19095
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## 287 2 19 33 52 58.78507
## 288 6 17 50 32 63.14296
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## 290 11 14 26 41 55.43918
## 291 16 16 29 58 68.59148
## 292 16 17 26 21 59.86254
## 293 1 10 52 57 56.07524
## 294 9 15 42 58 61.40395
## 295 28 10 40 45 64.67595
## 296 27 11 38 34 56.16822
## 297 6 10 38 53 63.98104
## 298 16 14 55 59 71.28396
## 299 29 19 48 30 79.89373
## 300 3 18 37 22 49.71593
## 301 20 15 21 25 55.53703
## 302 28 10 20 28 58.87869
## 303 27 10 24 25 59.40237
## 304 4 11 39 28 43.38461
## 305 19 18 30 58 70.36439
## 306 26 12 21 50 64.11290
## 307 3 10 22 51 41.45654
## 308 19 14 42 31 72.33673
## 309 20 16 31 27 63.25487
## 310 7 15 39 27 56.87439
## 311 20 10 24 47 59.41441
## 312 9 14 56 39 71.56737
## 313 1 14 57 26 56.80511
## 314 8 15 49 30 53.41584
## 315 7 12 28 39 49.69844
## 316 18 14 53 35 66.94322
## 317 8 16 54 23 58.85692
## 318 1 14 41 25 50.53182
## 319 11 14 43 41 67.59142
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## 321 25 19 29 22 67.44417
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## 323 18 12 42 30 62.00377
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## 328 28 12 28 32 64.30579
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## 335 17 16 23 37 54.49738
## 336 2 19 45 25 59.34289
## 337 1 19 48 37 69.04952
## 338 16 11 53 38 67.87336
## 339 12 15 42 41 56.92058
## 340 5 15 56 56 71.02301
## 341 5 12 30 51 56.68316
## 342 27 11 25 22 52.85109
## 343 23 10 37 47 64.32991
## 344 9 15 40 29 55.97344
## 345 9 14 55 54 65.77551
## 346 3 18 57 48 72.14140
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## 348 16 16 36 52 69.69766
## 349 16 14 56 26 62.96254
## 350 3 14 44 31 50.31271
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## 359 17 11 46 39 55.87172
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## 424 17 13 42 31 57.36560
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## 427 29 10 25 47 68.36792
## 428 14 12 27 56 53.88411
## 429 21 13 44 56 80.66344
## 430 6 17 37 31 53.64438
## 431 3 15 44 56 61.09316
## 432 28 19 31 47 73.06394
## 433 9 16 34 34 43.47880
## 434 5 17 45 25 60.82023
## 435 24 11 45 39 72.28422
## 436 29 19 51 33 81.99336
## 437 17 17 29 21 57.13495
## 438 14 12 35 28 56.07970
## 439 21 16 26 56 59.98727
## 440 3 12 36 39 45.85474
## 441 28 16 42 45 82.07132
## 442 1 11 45 48 46.62855
## 443 20 19 40 20 68.26779
## 444 29 15 41 54 73.11709
## 445 21 12 26 25 56.03215
## 446 23 12 33 56 67.92179
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## 448 3 16 26 52 58.02370
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## 450 25 19 27 31 63.54874
## 451 10 16 42 39 62.90285
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## 482 24 14 54 31 66.48405
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## 488 4 15 51 20 50.94104
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## 490 20 16 54 50 84.94223
## 491 1 13 58 35 58.46635
## 492 8 17 48 33 55.50861
## 493 14 16 48 24 68.19614
## 494 16 18 35 45 64.05928
## 495 14 16 29 47 70.36649
## 496 12 12 49 21 62.20024
## 497 19 12 44 25 58.86339
## 498 23 17 58 28 84.60508
## 499 15 14 39 52 66.23928
## 500 28 13 24 44 69.46387modelbesar <- lm(Gaji ~ Pengalaman + Pendidikan + Umur + Jam_Kerja, data = df)
summary(modelbesar)##
## Call:
## lm(formula = Gaji ~ Pengalaman + Pendidikan + Umur + Jam_Kerja,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.0785 -3.5513 -0.0003 3.1783 16.1362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.02790 1.64537 1.232 0.218
## Pengalaman 0.85112 0.02603 32.702 <2e-16 ***
## Pendidikan 1.15568 0.07815 14.788 <2e-16 ***
## Umur 0.50587 0.01964 25.762 <2e-16 ***
## Jam_Kerja 0.29655 0.01990 14.900 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.123 on 495 degrees of freedom
## Multiple R-squared: 0.8117, Adjusted R-squared: 0.8102
## F-statistic: 533.6 on 4 and 495 DF, p-value: < 2.2e-16