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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