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Ace Linear Regression Practical Exam
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Module 2
Assessing & Comparing Model Fit
1
Assessing Regression Fit: R-squared and RSE
Interpret R-squared and the Residual Standard Error (RSE) to assess the fit of a single regression model.
2
Understanding and Applying Adjusted R-squared
Explain the purpose of Adjusted R-squared and use it to compare multiple regression models with different numbers of predictors.
3
Choosing the Best Fit: Adjusted R-squared and RSE
Justify model preference by comparing the Adjusted R-squared and RSE of competing models.
4
Avoiding the R-squared Trap: Why More Variables Aren't Always Better
Articulate the common exam trap of adding variables to a model solely to increase R-squared.
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Core Concepts & R Output Navigation
Next module
The ANOVA Table & Overall Significance