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SLR emp_data.R
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SLR emp_data.R
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#3.Emp_data -> Build a prediction model for Churn_out_rate
Emp_data <- read.csv("~/Downloads/Data Science/data set/emp_data.csv")
summary(Emp_data)
var(Emp_data$Salary_hike)
sd(Emp_data$Salary_hike)
var(Emp_data$Churn_out_rate)
sd(Emp_data$Churn_out_rate)
library(e1071)
skewness(Emp_data$Salary_hike)
kurtosis(Emp_data$Salary_hike)
boxplot(Emp_data$Salary_hike)
barplot(Emp_data$Salary_hike)
hist(Emp_data$Salary_hike)
qqnorm(Emp_data$Salary_hike)
plot(Emp_data)
cor(Emp_data)
Model1 <- lm(Churn_out_rate ~ Salary_hike, data = Emp_data)
summary(Model1)
Model2 <- lm(Churn_out_rate ~ log(Salary_hike),data = Emp_data)
summary(Model2)
FinalModel <- lm(log(Churn_out_rate) ~ log(Salary_hike),data = Emp_data)
summary(FinalModel)
plot(FinalModel)