Which type of analysis provides the number of parameters that each input contributes to a regression model?

Type 3 Analysis of Effects is a statistical method commonly used in regression analysis that evaluates the effect of each individual variable (or input) on the model while accounting for the effects of other variables. It provides insights into the contribution of each input by examining the unique contribution adjusted for all other parameters in the model.

This type of analysis is particularly useful in identifying how many parameters each input contributes since it isolates the effect of each variable. By looking at Type 3 sums of squares, analysts can determine the significance of each predictor.

In contrast, Fit Statistics typically provide an overview of model performance metrics, such as R-squared values or root mean square error, which do not break down contributions on a per-variable basis. Variable Summary offers descriptive statistics about the data but does not specifically analyze the effect each variable has on the regression model. Model Information gives an overview of the model structure and how it was built, rather than detailing individual parameter contributions.

Therefore, for understanding the precise impact and contribution of each input variable in a regression context, Type 3 Analysis of Effects is the most appropriate choice.