Many widely-adopted college textbooks that are designed for a student's first (and possibly last) statistics course have incorporated new trends in statistical education, but are organized in a manner that is still driven by a traditional computational, rather than a conceptual, framework. An alternative approach allows for the treatment of many seemingly-unrelated conventional procedures such as one- and two-sample t-tests and analyses of variance and covariance under a unifying prediction model approach. Furthermore, this approach, combined with the power of modern statistical software packages, prepares the student to solve problems beyond the scope of traditional procedures. Students will appreciate the acquisition of practical research capabilities and might even be stimulated to continue their study of statistics.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education