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.These laboratory materials present applications in a variety of real-world settings,
with data from epidemiology, environmental sciences, medicine, social sciences, physical sciences, manufacturing,
engineering, marketing, and sports. The book includes a CD-ROM with 238 laboratory problems written as Mathematica notebooks, and introductions to built-in and custom Mathematica commands.
Mathematica Laboratories for Mathematical Statistics: Emphasizing Simulation and Computer Intensive Methods 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: This book is written with both the instructor and the student in mind. The order of topics and the level of presentation are similar to those of other mathematical statistics books. Thus, instructors will find it easy to incorporate this approach in their classroom.The accompanying student CD of laboratory activities, written as Mathematica notebooks, contains text, data, computations, and graphics. Mathematica notebooks are particularly well-suited for presenting concepts and problems, and for writing solutions. Over half of the 238 laboratory problems use real-world data, many from recent research reports or on-going research. Prerequisites include multivariable calculus and familiarity with the basics of set theory, vectors, matrices, and problem-solving using a computer.
Additional information: This work was supported in part by a grant from the National Science Foundation through its Division of Undergraduate Education. A review of the text appeared in SIAM Review. Information on the ASA-SIAM series on Statistics and Applied Probability can be obtained here.
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