Which statement is true about forward stepwise selection when n=100 and p=4?

Forward stepwise selection is a method used in statistical modeling to select a subset of predictors for a given model. When there are n=100 observations and p=4 predictors, forward stepwise selection starts by fitting no predictors in the model and then adds predictors one by one based on a selection criterion (like AIC, BIC, or adjusted R-squared) until the best combination is found.

In this specific case, when starting from no predictors, the model will be fit sequentially by adding one of the four predictors at a time. This means that the first model will consist of one predictor, the next will include a second predictor along with the first, continuing until all predictors are included in separate models. The total number of distinct models that will be fitted is equal to the number of combinations of predictors from none (zero predictors) to all (four predictors).

With four predictors, the sequence of fits includes:

1. No predictors

2. One predictor only (4 possibilities)

3. Two predictors together (combinations of 2 out of 4, which is 6)

4. Three predictors (combinations of 3 out of 4, which is 4)

5. All four predictors (1 possibility)

Therefore