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A Complete Tutorial on Linear Regression with R

This article explains how to run linear regression with R.

Table of Contents:

  • Linear Regression
  • Variable Type
  • Simple vs. Multiple Linear Regression
  • Regression Equation
  • Important Term : Residual
  • Algorithm
  • Minimum Sample Size
  • Assumptions of Linear Regression Analysis

                1. Linear Relationship

                2. Normality of Residual

                3. Homoscedasticity

                4. No Outlier Problem

                5. Multicollinearity

                6. Independence of error terms - No Autocorrelation

  • Standardized Coefficients
  • Measures of Model Performance

                 1. R-squared

                       Rule

                 2. Adjusted R-squared

                       Adjusted R-Squared is more important metrics than R-squared

                 3. RMSE ( Root Mean Square Error)

                       Rule

                       R-Squared vs RMSE

  • R Code : Linear Regression

Check out all this information, here. For more articles about linear regression, click here and about R, click here.

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Comment by Sailesh Ganeshan on July 15, 2016 at 5:05pm

Thanks for the handy reference!

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