Analisa Pengaruh IG Kuliner Kota Batam
Fendi Hidayat 9/16/2020 library(readxl)
KKB <- read_excel("~/Downloads/KKB.xlsx")
# 1. Melakukan Regresi
ModelKKB = lm(Y~X, data = KKB)
# UJI KLASIK
# 1. uji normalitas
# 1.a. Dengan QQ Plot library(devtools)
## Loading required package: usethis library(usethis)
library(ryouready) library(ggplot2)
qqresidual = qqnorm_spss(ModelKKB$residuals, method = 1, ties.method =
"average")
ggplot(qqresidual)
# Jika p-value > 0.05 maka berdistribusi normal
ks.test(ModelKKB$residuals, ecdf(ModelKKB$residuals))
## Warning in ks.test(ModelKKB$residuals, ecdf(ModelKKB$residuals)):
ties should
## not be present for the Kolmogorov-Smirnov test
##
## One-sample Kolmogorov-Smirnov test
##
## data: ModelKKB$residuals
## D = 0.037037, p-value = 0.9999
## alternative hypothesis: two-sided
# 2. UJI Multikolinieritas
#olsrr::ols_vif_tol(ModelKKB)
# 3. UJI AUTOKORELASI DURBIN WATSON
#install.packages("lmtest") #install package untuk menggunakan fungsi durbinwatson
# : Jika nilai p-value > 0,05 (nilai alpha) maka tidak terjadi autokorelasi
lmtest::dwtest(ModelKKB)
##
## Durbin-Watson test
##
## data: ModelKKB
## DW = 1.6173, p-value = 0.04014
## alternative hypothesis: true autocorrelation is greater than 0
# 4. UJI HETEROKEDASTISITAS
# Jika nilai p-value > 0,05 (nilai alpha) maka tidak terjadi heterokedastisitas
lmtest::bgtest(ModelKKB)
##
## Breusch-Godfrey test for serial correlation of order up to 1
##
## data: ModelKKB
## LM test = 2.3451, df = 1, p-value = 0.1257 summary(ModelKKB)
##
## Call:
## lm(formula = Y ~ X, data = KKB)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.834 -1.944 0.239 2.721 6.575
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3661 3.3594 1.002 0.319
## X 0.5183 0.0673 7.701 3.33e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.494 on 79 degrees of freedom
## Multiple R-squared: 0.4288, Adjusted R-squared: 0.4215
## F-statistic: 59.3 on 1 and 79 DF, p-value: 3.332e-11
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