Statistics is the science that focuses on drawing conclusions from data, by modeling and analyzing the data using probabilistic models. In An Introduction to Mathematical Statistics, the authors describe key concepts from statistics and give a mathematical basis for important statistical methods. Much attention is paid to the sound application of those methods to data. The three main topics in statistics are estimators, tests, and confidence regions. The authors illustrate these in many examples, with a separate chapter on regression models, including linear regression and analysis of variance. They also discuss the optimality of estimators and tests, as well as the selection of the best-fitting model. Each chapter ends with a case study in which the described statistical methods are applied. This book assumes a basic knowledge of probability theory, calculus, and linear algebra.
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 ...