Scatterplots

3 Concepts
Simple Linear Regression

5 Concepts
Analysis of Variance (ANOVA)

6 Concepts
Summary of Fit

3 Concepts
Parameter Estimates

1 Concept
Multiple LInear Regression (Leonard)

2 Concepts
Confidence Intervals (Part 2)

3 Concepts
Prediction Intervals

2 Concepts
The primary places you want to look when comparing models are...

**Adjusted R**^{2}**Root Mean Square Error (RMSE)**

The *higher* the **Adjusted R ^{2}**, the more variability that is explained by the model!

The *lower* the **Root Mean Square Error (RMSE)**, the less error in predicting each of the observations exists!

**Scenario**: Which model is better? Model 1 or 2? And why?

*Model 1*

*Model 2*

**Adjusted R ^{2}**

0.78621 (Model 1) < 0.86731 (Model 2)

**RMSE**

43.8180 (Model 1) > 34.5210 (Model 2)

**Answer**: Model 2 is better, as it has a *higher* Adjusted R^{2} and *lower* Root Mean Square Error (RMSE).

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