Question

 

1. The best measure for model selection is the Adjusted R-square.
2. Partial sums of squares are more useful than sequential sums of squares.
3. If we have a categorical variable with 4 categories we will need 4 dummy variables to model this.

 

 

Answer 1

A) use check your model good fit or not fit by using R square and adjusted R square

but most likely Adjusted R square use

Beacouse of you add some anather independent variable then r square is increased bcoz more information you added,

but Adjusted R square it can be increase or not. If added independent variable is if truly affected on y then they increase other wise no..

so we use Adjusted R square ..

B)

Can the adjusted sums of squares be less than, equal to, or greater than the sequential sums of squares?

The adjusted sums of squares can be less than, equal to, or greater than the sequential sums of squares.

Suppose you fit a model with terms A, B, C, and A*B. Let SS (A,B,C, A*B) be the sum of squares when A, B, C, and A*B are in the model. Let SS (A, B, C) be the sum of squares when A, B, and C are included in the model. Then, the adjusted sum of squares for A*B, is:

SS(A, B, C, A*B) - SS(A, B, C)

However, with the same terms A, B, C, A*B in the model, the sequential sums of squares for A*B depends on the order the terms are specified in the model.

Using similar notation, if the order is A, B, A*B, C, then the sequential sums of squares for A*B is:

SS(A, B, A*B) - SS(A, B)

C)You do not convert categorical variables into continous variables to use them in regression models. You use them as categorical (not necessarily being binary!). You must make multiple dummy variables from them, not to put them directly as single variables. But there are many different ways in making dummy variables, each has a different meaning and purpose.

thanks