Statistics Using Stata
An Integrative Approach
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- Sharon Lawner Weinberg, New York University
- Sarah Knapp Abramowitz, Drew University, New Jersey
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Engaging and accessible to students from a wide variety of mathematical backgrounds, Statistics Using Stata combines the teaching of statistical concepts with the acquisition of the popular Stata software package. It closely aligns Stata commands with numerous examples based on real data, enabling students to develop a deep understanding of statistics in a way that reflects statistical practice. Capitalizing on the fact that Stata has both a menu-driven 'point and click' and program syntax interface, the text guides students effectively from the comfortable 'point and click' environment to the beginnings of statistical programming. Its comprehensive coverage of essential topics gives instructors flexibility in curriculum planning and provides students with more advanced material to prepare them for future work. Online resources - including complete solutions to exercises, PowerPoint slides, and Stata syntax (do-files) for each chapter - allow students to review independently and adapt codes to solve new problems, reinforcing their programming skills.Read more
- Can be used in courses at the undergraduate or graduate level in the social, behavioral, health, and policy sciences
- Covers topics not included in other introductory texts, such as robust methods of estimation based on the bootstrap resampling technique, regression to the mean, and diagnostic tools for analyzing model fit
- Uses an extremely clear and lively writing style and worked examples that will interest students
- Online features include problem sets and appendices for students, and PowerPoint slides and a test bank for instructors
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- Date Published: July 2017
- format: Adobe eBook Reader
- isbn: 9781316680162
- contains: 216 b/w illus. 64 tables 420 exercises
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
2. Examining univariate distributions
3. Measures of location, spread, and skewness
4. Re-expressing variables
5. Exploring relationships between two variables
6. Simple linear regression
7. Probability fundamentals
8. Theoretical probability models
9. The role of sampling in inferential statistics
10. Inferences involving the mean of a single population when σ is known
11. Inferences involving the mean when σ is not known: one- and two-sample designs
12. Research design: introduction and overview
13. One-way analysis of variance
14. Two-way analysis of variance
15. Correlation and simple regression as inferential techniques
16. An introduction to multiple regression
17. Nonparametric methods.
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