Springer texts in statistics

The present text introduces the student to the basic ideas of estimation and hypothesis testing early in the course after a rather brief introduction to data organization and some simple ideas about probability. Estimation and hypothesis testing are discussed in terms of the two-sample problem. The book exploits nonparametric ideas that rely on nothing more complicated than sample differences Y-X, referred to as elementary estimates, to define the Wilcoxon-Mann-Whitney test statistics and the related point and interval estimates. The ideas behind elementary estimates are then applied to the one-sample problem and to linear regression and rank correlation. Discussion of the Kruskal-Wallis and Friedman procedures for the k-sample problem rounds out the nonparametric coverage. The concluding chapters provide a discussion of Chi-square tests for the analysis of categorical data and introduce the student to the analysis of binomial data including the computation of power and sample size. Most chapters in the book have an appendix discussing relevant Minitab commands.

"This book is for a first course on stochastic processes to be taken by undergraduates or masters students who have had a course in probability theory, but who have not had a course in measure theory. It covers Markov chains in discrete and continuous time, Poisson processes, renewal theory, and Brownian motion and martingales. The last two topics are important for the brief treatment of option pricing."--BOOK JACKET. "The book presents only the essentials of the subject, the parts of the theory most important for applications. To allow readers to choose their own level of detail, many of the proofs begin with a nonrigorous answer to the question "Why is this true?" followed by a proof that fills in the missing details."--BOOK JACKET.