This article explains how to run linear regression with R.
Table of Contents:
1. Linear Relationship
2. Normality of Residual
3. Homoscedasticity
4. No Outlier Problem
5. Multicollinearity
6. Independence of error terms - No Autocorrelation
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
Check out all this information, here. For more articles about linear regression, click here and about R, click here.
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