Data Analysis Using SAS Enterprise Guide
Data Analysis Using SAS Enterprise Guide
Glenn Gamst, University of La Verne, California
A. J. Guarino, Alabama State University
This book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, health, social work, sociology, and many other fields.
- Basic explanation of the SAS Enterprise Guide
- Can be used as part of a course or as a stand-alone learning resource
- Suitable for use across the social sciences
Product details
November 2009Adobe eBook Reader
9780511601842
0 pages
0kg
340 b/w illus. 8 colour illus.
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- Part I. Introducing SAS Enterprise Guide:
- 1. SAS Enterprise Guide projects
- 2. Placing data into SAS Enterprise Guide projects
- Part II. Performing and Viewing Output:
- 3. Performing statistical analyses in SAS Enterprise Guide
- 4. Managing and viewing output
- Part III. Manipulating Data:
- 5. Sorting data and selecting cases
- 6. Recoding existing variables
- 7. Computing new variables
- Part IV. Describing Data:
- 8. Descriptive statistics
- 9. Graphing data
- 10. Standardizing variables based on the sample data
- 11. Standardizing variables based on existing norms
- Part V. Score Distribution Issues:
- 12. Detecting outliers
- 13. Assessing normality
- 14. Nonlinearly transforming variables in order to meet underlying assumptions
- Part VI. Correlation and Prediction:
- 15. Bivariate correlation: Pearson product moment and Spearman rho correlations
- 16. Simple linear regression
- 17. Multiple linear regression
- 18. Simple logistic regression
- 19. Multiple logistic regression
- Part VII. Comparing Means t Tests:
- 20. Independent groups t test
- 21. Correlated samples t test
- 22. Single sample t test
- Part VIII. Comparing means ANOVA:
- 23. One-way between subjects analysis of variance
- 24. Two-way between subjects design
- 25. One-way within subjects analysis of variance
- 26. Two-way mixed ANOVA design
- Part IX. Nonparametric Procedures:
- 27. One-way chi square
- 28. Two-way chi square
- 29. Nonparametric between subjects one-way ANOVA
- Part X. Advanced ANOVA Techniques:
- 30. One-way between subjects analysis of covariance
- 31. One-way between subjects multivariate analysis of variance
- Part XI. Analysis of Structure:
- 32. Factor analysis
- 33. Canonical correlation analysis.
