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 inclass 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, sports. 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.
Preface; 1. Introductory probability concepts; 2. Discrete probability distributions; 3. Continuous probability distributions; 4. Mathematical expectation; 5. Limit theorems; 6. Transition to statistics; 7. Estimation theory; 8. Hypothesis testing theory; 9. Order statistics and quantiles; 10. Two sample analysis; 11. Permutation analysis; 12. Bootstrap analysis; 13. Multiple sample analysis; 14. Linear least squares analysis; 15. Contingency table analysis; Bibliography; Index.
This text presents a concise introduction to the concepts of probability theory and mathematical statistics.
Jenny Baglivo's book and its Mathematica labs now make it easy to teach a modern course that better prepares students for contemporary statistical thinking and practice. That alone would be a major contribution to statistics education, but this book offers more: it is thoughtfully organized and unusually well-crafted. For example, theorems are given helpful descriptive names, and are often presented in ways that highlight parallel structure and make the big picture easier to see. George W. Cobb, Mount Holyoke College I particularly value that the emphasis of the labs is on the statistical concepts and not on programming in Mathematica. The Mathematica tools and needed commands are carefully developed so that students with a minimal knowledge of the Mathematica environment can focus on the ideas while those who have more experience with Mathematica can utilize its power. This text is an important addition to materials for the post-calculus probability and statistics courses. Adele Marie Rothan, College of St. Catherine
