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MultipleLinearRegression.r
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MultipleLinearRegression.r
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tourism <- read.csv("C:/Users/praneetha/Desktop/statistics/data_analysis1/Exams/project/project_org.csv",sep=",",header=TRUE)
par(mex=0.5)
pairs(tourism,gap=0,cex.labels = 0.9)
########## fitting a regression line ############
fit<-lm(best_destination~Climate+Housing+HealthCare+Crime+Transportation+Education+Arts+Recreation+economy,data=tourism)
summary(fit)
anova(fit)
############Checking for normality ########
plot(fitted(fit),resid(fit))
qq<-qqnorm(resid(fit))
#############Comparing 2 models ##########
m1<-lm(best_destination~Climate+Housing+HealthCare+Crime+Transportation+Education+Arts+Recreation+economy,data=tourism)
m2<-lm(best_destination~Climate+Housing+HealthCare+Transportation+Education+Arts+economy,data=tourism)
anova(m1,m2)
######checking for constant error variance#########
var.test(m1,m2)
#########Checking linearity##############
library(car)
cr.plots(fit)
cr.plots(fit,ask=FALSE)
############ Correlation test #################
cor.test(tourism$best_destination,tourism$Climate+tourism$Housing+tourism$HealthCare+tourism$Crime+tourism$Transportation+tourism$Education+tourism$Arts+tourism$Recreation+tourism$economy)
######### spotting multi collinearity ############
spreadLevelPlot(fit)
vif(fit)
########outlier test#########
lev=hat(model.matrix(fit))
plot(lev)
###### Backward model selection ##########3
step(m1,data=tourism,direction = "backward")
######### Predict ################
predicted<-predict(m2, data=tourism)
sort(predicted)
library(xlsx)
write.xlsx(predicted,"predicted.xlsx")