8.8 Absenteeism, Part II. Exercise 8.4 considers a model that predicts the numbe

Question

8.8 Absenteeism, Part II. Exercise 8.4 considers a model that predicts the number of days absent using three predictors: ethnic background (eth), gender (sex), and learner status (1rn) The table below shows the adjusted R-squared for the model as well as adjusted R-squared values for all models we evaluate in the first step of the backwards elimination process. Model 1 Full model 2 No ethnicity 3 No sex 4 Adjusted R 0.0701 0.0033 0.0676 0.0723 No learner status Which, if any, variable should be removed from the model first?

Explanation / Answer

The adjusted R-squared for the full model is 0.0701

If we remove ethnicity from the full model in the first step of the backwards elimination process,

The adjusted R-squared for the model drops down to - 0.0033

If we remove sex from the full model in the first step of the backwards elimination process,

The adjusted R-squared for the model drops down to 0.0676

However,

If we remove learner status from the full model in the first step of the backwards elimination process,

The adjusted R-squared for the model increases to 0.0723

So,

the removal of variable - learner status in the first step of the backward elimination process,

leads to an improvement in the R- square from the full mode.

Hence, learner status variable

will be removed from the full model in the first step of the backward elimination process.

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