Statistical Inference: A Short Course

"Statistical Inference: A Short Course" is general in nature and is appropriate for undergraduates and first-year graduates majoring in the natural sciences, the social sciences, or in business. The book offers sufficient material for a one-semester course without overwhelming students with "too fas [...]
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"Statistical Inference: A Short Course" is general in nature and is appropriate for undergraduates and first-year graduates majoring in the natural sciences, the social sciences, or in business. The book offers sufficient material for a one-semester course without overwhelming students with "too fast a pace" or "too many" topics. The essential topics appear in the main body of the chapters and additional interesting topics can be found in the chapter appendices and chapter exercises. While Chapters 1-10 are fundamental to any basic statistics course, instructors can tailor their course and choose additional topical coverage from Chapters 11-13. This latter set of chapters is optional, and the topics therein can be selected with an eye towards student interests and needs. While most beginning statistics books discuss the concepts of simple random sampling and normality, this book takes such discussions a bit further. Specifically, key assumptions typically made in the areas of estimation and testing are a "random sample" of observations drawn from a "normal population." However, given a particular data set, how can it be determined that it actually constitutes a random sample and, secondly, how can it be concluded if the parent population can be taken to be normal? That is, can we proceed "as if" the sample is random? And can we operate "as if" the population is normal? Answers to these questions are provided by formal test procedures for randomness and for the assessment of normality. Other unique topics found in the book include determining a confidence interval for a population median, ratio estimation (a technique akin to estimating a population proportion), general discussions of randomness and causality, and nonparametric methods that serve as an alternative to parametric routines when the latter are not strictly applicable.