Stats Modeling Exam 2

Three types of Anova Tables

Type 1: Sequential Sum of Squares

Type 2: Hierarchical Sum of Squares

Type 3: Marginal Anova

Sequential Sum of Squares (What is it? df for predictors and residuals? Compresses to what?)

Additional variability explained when new variable is added to the model (sequentially adding)

Predictors df is how many slopes were needed to include it in the model (always 1 for quantitative)

Residuals: n-k-1

Compresses to overall anova table (model row has all the predictors combined, residuals row is just residuals)

Hierarchical (What is it? Matches thing…)

Additional variability explained by adding this new variable to a model containing everything else

P values match the table of coefficients

Maginal Anova

Like hierarchical, but with interaction terms

VIF Above ___ is typically bad

above 5 (r² > 0.8)

Note about predictions from a model with multicollinearity

Predictions are fine, but individual coefficient conclusions are not

“Good” for mallow cp

<= m+1

CP, AIC, BIC preference for small models

CP and AIC are moderate, BIC prefers small ones a lot

Methods of picking models (4)

Best Subsets - fits all 2^k models and picks best basedon criterion

Backwards Elimination - starts with full until deleting a term doesn’t improve it (succeptible to multicollinearity)

Forward Selection - Keep adding until no longer improves

Stepwise Regression - adds stuff with forward, but also checks with backwards if something can be remove

Nested F-test

“Is anything gained by adding these terms to a smaller model”