More Problem Solving Power: Exploiting Prediction Models and Statistical Software in a One-Semester Course

Abstract

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.

• Linear models
• Undergraduate teaching techniques

Acknowledgement

The authors wish to thank Earl Jennings [University of Texas at Austin] and Robert Mason [Southwest Research Institute] for their many helpful comments. They also thank the referees for suggesting many improvements to the manuscript.