Applied Statistics II: Multivariable and Multivariate Techniques
β Scribed by Rebecca M. Warner
- Publisher
- SAGE Publications, Inc
- Year
- 2020
- Tongue
- English
- Leaves
- 713
- Edition
- 3
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Rebecca M. Warnerβs bestselling Applied Statistics: From Bivariate Through Multivariate Techniques has been split into two volumes for ease of use over a two-course sequence. Applied Statistics II: Multivariable and Multivariate Techniques, Third Edition is a core multivariate statistics text based on chapters from the second half of the original book.Β
The text begins with two new chapters: an introduction to the new statistics, and a chapter on handling outliers and missing values. All chapters on statistical control and multivariable or multivariate analyses from the previous edition are retained (with the moderation chapter heavily revised) and new chapters have been added on structural equation modeling, repeated measures, and on additional statistical techniques. Each chapter includes a complete example, and begins by considering the types of research questions that chapterβs technique can answer, progresses to data screening, and provides screen shots of SPSS menu selections and output, and concludes with sample results sections. By-hand computation is used, where possible, to show how elements of the output are related to each other, and to obtain confidence interval and effect size information when SPSS does not provide this. Datasets are available on the accompanying website.
π SIMILAR VOLUMES
<strong>Rebecca M. Warner's Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition</strong>provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and bina
This book focuses on when to use the various analytic techniques and how to interpret the resulting output from the most widely used statistical packages (e.g., SAS, SPSS).
This book focuses on when to use the various analytic techniques and how to interpret the resulting output from the most widely used statistical packages (e.g., SAS, SPSS).