This book provides an introduction to statistical inference: parameter estimation, confidence intervals, and hypothesis testing. Classical z-, t- and F-tests are discussed, along with alternatives for situations involving small numbers of data points and non-normal data. The focus is first on inference for one population. Next, the focus shifts to two and to more than two independent populations. Also, paired data are discussed in detail. The book combines mathematical depth with numerous examples and demonstrations using the JMP software.
- Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested.
- Pays attention to the usual parametric hypothesis tests (z-, t-, chi square and F-tests) as well as to non-parametric tests for small and non-normal data (including the calculation of exact p-values).
- Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic.
- Promotes the use of graphs and confidence intervals in addition to p-values.
- Provides the statistical theory including detailed mathematical derivations.
- Presents illustrative examples accompanied by step-by-step instructions and screenshots to help develop the reader’s understanding of both the statistical theory and its applications.
- A supporting website with data sets.
- A one-year license for JMP’s student edition.
Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics in all of these areas.