"Fundamentals of Mathematical Statistics is meant for a standard one-semester advanced undergraduate or graduate level course on Mathematical Statistics. It covers all the key topics - statistical models, linear normal models, exponential families, estimation, asymptotics of maximum likelihood, significance testing, and models for tables of counts. It assumes a good background in mathematical analysis, linear algebra, and probability, but includes an appendix with basic results from these areas. Throughout the text, there are numerous examples and graduated exercises that illustrate the topics covered, rendering the book suitable for teaching or self-study. Features a concise, yet rigorous introduction to a one-semester course on mathematical statistics. Thus, this textbook will be a perfect fit for an advanced course on mathematical statistics or statistical theory. The concise and lucid approach means it could also serve as a good alternative, or supplement, to existing texts"--
The Selected Papers of E. S. Pearson
Continuing its proven approach, the Seventh Edition has been updated with new examples, exercises, and content for an even stronger presentation of the material.
This classic text retains its outstanding features and continues to provide students with excellent background in the mathematics of statistics. Extensively revised with three new chapters.
Statistics
Techniques are introduced through examples, showing how statistics has helped to solve major problems in political science, psychology, genetics, medicine, and other fields.
Noted for its integration of real-world data and case studies, this text offers sound coverage of the theoretical aspects of mathematical statistics.
"A high school book written to help students make sense of the world with statistics."--
This Pearson Original edition is published for Macquarie University.
... the student should be able to : • draw a scatter diagram and a line of best fit • distinguish between positive and negative correlation • calculate covariance • calculate Pearson's product moment correlation coefficient calculate ...
The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in ...