Range or variation :

   Step 1 : Import the data

   Step 2 : Check Correlation between the variables

   Step 3 : Create a relationship model using lm() function in R

   Step 4 : Summary of the linear model using summary() function

  Step 5: Check normality o the residuals.

   Step 6 : Visualizing the regression graphically.

   Step 7 : Predicting the dependent variable for two or more independent variable using predict() function.

#Multiple Linear Regression Model

#Get and Set Working Directory
print(getwd())
setwd(“/home/soft13”)
getwd()

#Read file from Excel
#install.packages(“xlsx”)
library(“xlsx”)
data<-read.xlsx(“mtcars.xlsx”,sheetIndex=1)
data1<-mtcars[,c(“mpg”,”disp”,”hp”,”wt”)]
View(data1)

#Correlation Test
#install.packages(“psych”)
library(“psych”)
corr.test(data1)

#Multiple Linear Regression Model

linear_model<-lm(mpg~disp+hp+wt,data=data1)
print(linear_model)
summary(linear_model)

#Check Normality
#Histogram
hist(linear_model$residuals,col=75)
#install.packages(“nortest”)
library(“nortest”)
#Anderson – Darling Test
ad.test(linear_model$residuals)

#Regression Diagnostics
par(mfrow = c(2, 2))
plot(linear_model)

#Confidence Interval
confint(linear_model)

#Prediction
find<-data.frame(disp=150,hp=170,wt=3.089)
predict(linear_model,find)