Introductory probability and statistical applications/ Paul L. Meyer

Includes index

Introduction to Probability -- Finite Sample Spaces -- Conditional Probability and Independence -- One-Dimensional Random Variables -- Functions of Random Variables -- Two and Higher-Dimensional Random Variables -- Two- and Higher-Dimensional Random Variables -- The Poisson and Other Discrete Random Variables -- Some Important Continuous Random Variables -- The Moment-Generating Function -- Applications to Reliability Theory -- Sums of Random Variables -- Samples and sampling distributions -- Estimation of Parameters -- Testing hypotheses

Many mathematical topics may be introduced at varying stages of difficulty, and this is certainly true of probability. In this text, an attempt is made to take advantage of the reader's mathematical background without exceeding it. Precise mathematical language is used but care is taken not to become excessively immersed in unnecessary mathematical details. Although a number of concepts are introduced and discussed in an informal manner, definitions and theorems are carefully stated. If a detailed proof of a theorem is not feasible or desirable at least an outline of the important ideas is provided. A distinctive feature of this text is the "Notes" following most of the theorems and definitions. In these notes the particular result or concept being presented is discussed from an intuitive point of view.