A modern introduction to probability and statistics : understanding why and how
著者
書誌事項
A modern introduction to probability and statistics : understanding why and how
（Springer texts in statistics）
Springer, c2010
- : [paperback]
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注記
Bibliography: p. [475]-476
Includes index
内容説明・目次
内容説明
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
目次
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.
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