Day #2 - Multivariate Linear Regression - R

• More than one predictor involved in this case

	#x,y,z with random predictors
	x = 1:20 + rnorm(100,sd=3)
	y = 1:20 + rnorm(100,sd=2)
	z = 1:20 + rnorm(100,sd=3)
	#correlation between x and y
	cor(x,y)
	#correlation between y and z
	cor(y,z)
	#linear regression with two variables
	model = lm(y~x+z)
	#Equation is
	#(Intercept) x z
	# 1.8885 0.3965 0.3587
	model
	#y = 1.885 + 0.3965x + 0.3587z
	err = residuals(model)
	plot(model$fitted.values,err)
	#This will look like normal distribution
	hist(err)

view raw multivariableregression.r hosted with ❤ by GitHub

Happy Learning!!!