Mathematica Laboratories for Mathematical Statistics:
Emphasizing Simulation and Computer Intensive Methods,
by Jenny A. Baglivo
ASA-SIAM Series on Statistics and Applied Probability, Volume 14,
published 2005; updated 2012, 2019
Project Description: Integrating computers into mathematical statistics courses allows students to simulate experiments and visualize their results, handle larger data sets, analyze data more quickly, and compare the results of classical methods of data analysis with those using alternative techniques. This text presents a concise introduction to the concepts of probability theory and mathematical statistics. The accompanying in-class and take-home computer laboratory activities reinforce the techniques introduced in the text and are accessible to students with little or no experience with Mathematica. The more than 230 laboratory problems present applications in many real-world settings, with data from the social and physical sciences, as well as manufacturing, marketing and sports. Originally published in 2005 using Mathematica Version 5, the lab materials were updated to Version 7 in 2012, and to Version 11 in 2019.
Mathematica Laboratories for Mathematical Statistics includes parametric, nonparametric, permutation, bootstrap and diagnostic methods. Chapters on permutation and bootstrap techniques follow the formal inference chapters and precede the chapters on intermediate-level topics. Permutation and bootstrap methods are discussed side by side with classical methods in the later chapters.
Audience: The materials were written to be used in the mathematical statistics sequence given at most colleges and universities (two courses of four semester hours each or three courses of three semester hours each). The materials can also be used for self-study by statistical practitioners or consultants interested in a computer based introduction to mathematical statistics, especially to computer intensive methods. Multivariable calculus and familiarity with the basics of set theory, vectors and matrices, and problem solving using a computer are assumed.
Additional Information and Acknowledgements: The initial work on this project was supported in part by a grant from the National Science Foundation (NSF) through its Division of Undergraduate Education, and in part by research incentive grants from Boston College. Dr. Sarah Quebec Fuentes, Texas Christian University, worked with me for several years when she was studying at Boston College and provided thoughtful advice at each step of development. The project was published jointly by the ASA (American Statistical Association) and SIAM (Society for Industrial and Applied Mathematics) in 2005. The electronic materials were updated in 2012 with the support of Boston College through its Academic Technology Innovation Grant (ATIG) program, and again in 2019. The 2012 updates were co-written with Massasoit Community Professor Alex Cotter.