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Praise for the first edition:
"One of my biggest complaints when I teach introductory statistics classes is that it takes me most of the semester to get to the good stuff-inferential statistics. The author manages to do this very quickly....if one were looking for a book that efficiently covers basic statistical methodology and also introduces statistical software [this text] fits the bill." -The American Statistician
Applied Statistical Inference with MINITAB, Second Edition distinguishes itself from other introductory statistics textbooks by focusing on the applications of statistics without compromising mathematical rigor. It presents the material in a seamless step-by-step approach so that readers are first introduced to a topic, given the details of the underlying mathematical foundations along with a detailed description of how to interpret the findings, and are shown how to use the statistical software program Minitab to perform the same analysis.
Gives readers a solid foundation in how to apply many different statistical methods.
MINITAB is fully integrated throughout the text.
Includes fully worked out examples so students can easily follow the calculations.
Presents many new topics such as one- and two-sample variances, one- and two-sample Poisson rates, and more nonparametric statistics.
Features mostly new exercises as well as the addition of Best Practices sections that describe some common pitfalls and provide some practical advice on statistical inference.
This book is written to be user-friendly for students and practitioners who are not experts in statistics, but who want to gain a solid understanding of basic statistical inference. This book is oriented towards the practical use of statistics. The examples, discussions, and exercises are based on data and scenarios that are common to students in their everyday lives.
List of contents
Introduction. Graphing Variables. Descriptive Representations of Data and Random Variables. Basic Statistical Inference for One-Sample. Basic Statistical Inference for Two Samples. Simple Linear Regression. More on Simple Linear Regression. Multiple Regression Analysis. More on Multiple Regression Analysis. One-Way Analysis of Variance (One-Way ANOVA). Additional Topics.
About the author
Sally A. Lesik is a professor of mathematics at Central Connecticut State University. Dr. Lesik has taught many mathematics, statistics, engineering, and physics courses. Her primary research is in applied statistical inference.
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Summary
Covers material typically presented in an intermediate statistics course at the undergraduate level and a first course in applied statistics at the graduate level. Topics include basic statistical inference, regression, and ANOVA. Advanced topics include non-parametric statistics, logistic regression, and goodness-of-fit tests.
