# .

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.

DSC Resources

Additional Reading

Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge

Views: 10514

Comment

### You need to be a member of Data Science Central to add comments!

Join Data Science Central

## Related TechTarget Content

Posted 7 June 2021