how to creat r output for simple linear regression equation

Residual standard error: 2.839 on 29 degrees of freedom Lm(formula = mpg ~ carb + wt, data = mtcars) We'll do this by creating a regression model in R, then we'll use the coefficients from that model and calculate the R-squared using our function and see if it matches the output from lm: # Create regression model First let's see if our function matches the R-squared calculated by lm. # Calculate and return the R-squared for the regression model SStot = sum((mtcars$mpg - mean(mtcars$mpg))^2) SSresid = sum(((a + b*mtcars$carb + c*mtcars$wt) - mtcars$mpg)^2) # Calculate the residual sum of squares from the regression model a, b, and c are the three regression parameters that we need to provide to the function. We'll use the mtcars data set, which is built into R.Īssume a regression model that predicts 'mpg' using the independent variables 'carb' and 'wt'.

Method will be more efficient in the long run, but just for illustration, here's a simple example of how to create your own function to calculate R-squared for any regression coefficients you choose.

How to creat r output for simple linear regression equation