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