For a data analysis course, I am looking for multivariable (multiple-predictors) real datasets (no MC simulations), for which regression models are a reasonable description and on which model selection methods (all subsets AIC or cross-validated R^2, stepwise ANOVA selection) can be compared.

Ideally, the datasets should have the following properties:

  1. No (or omittable) categorial predictors, because I would like to discuss the encoding schemes of nominal features and approaches for dealing with them at some later point.
  2. Not too many predictors (less than eleven), so that all-subset model selection is feasible.
  3. No missing values, because I would like to discuss imputation methods at some later point.

Ideally, I am looking for at least two datasets each for ordinary regression and logistic regression.

Any suggestions?

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