Data Analysis Using SAS Enterprise Guideby Lawrence S. Meyers, Glenn Gamst, A. J. Guarino
Pub. Date: 08/31/2009
Publisher: Cambridge University Press
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
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.
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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.
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