data cars
summary(cars)
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
Rata-rata kecepatan mobil adalah 15.4 km/jam.
Rata-rata jarak tempuh mobil adalah 42.98 km/jam.
##model regresi
model=lm(dist~speed, data=cars)
summary(model)
##
## Call:
## lm(formula = dist ~ speed, data = cars)
##
## 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 *
## 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-12
Persamaan Model Regresi: \[ y= \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \epsilon \] Interpretasi model:
Model Akhir: \[ y= -17.5790949 + 3.9324088x_1 \] \[ y= -17.579 + 3.932x_1 \]
#Uji Normalitas Residual
error = model$residuals
ks.test(error, "pnorm", mean(error), sqrt(var(error)))
##
## Asymptotic one-sample Kolmogorov-Smirnov test
##
## data: error
## D = 0.12957, p-value = 0.3708
## alternative hypothesis: two-sided
model ini memenuhi asumsi normalitas residual dengan p-value > 0.05
library(lmtest)
dwtest(model)
##
## Durbin-Watson test
##
## data: model
## DW = 1.6762, p-value = 0.09522
## alternative hypothesis: true autocorrelation is greater than 0
karena p-value > 0.05 maka gagal menolak H0. tidak terdeteksi autokorelasi pada model.
Scatter plot dari dist berdasarkan speed
Scatterplot jarak vs kecepatan
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.