A modern introduction to probability and statistics : understanding why and how
Author(s)
Bibliographic Information
A modern introduction to probability and statistics : understanding why and how
（Springer texts in statistics）
Springer, c2010
- : [paperback]
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Note
Bibliography: p. [475]-476
Includes index
Description and Table of Contents
Description
Suitable for self study
Use real examples and real data sets that will be familiar to the audience
Introduction to the bootstrap is included - this is a modern method missing in many other books
Table of Contents
Why probability and statistics?.- Outcomes, events, and probability.- Conditional probability and independence.- Discrete random variables.- Continuous random variables.- Simulation.- Expectation and variance.- Computations with random variables.- Joint distributions and independence.- Covariance and correlation.- More computations with more random variables.- The Poisson process.- The law of large numbers.- The central limit theorem.- Exploratory data analysis: graphical summaries.- Exploratory data analysis: numerical summaries.- Basic statistical models.- The bootstrap.- Unbiased estimators.- Efficiency and mean squared error.- Maximum likelihood.- The method of least squares.- Confidence intervals for the mean.- More on confidence intervals.- Testing hypotheses: essentials.- Testing hypotheses: elaboration.- The t-test.- Comparing two samples.
by "Nielsen BookData"