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Data Analysis Using SAS
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Data Analysis Using SAS

October 2008 | 640 pages | SAGE Publications, Inc
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Data Analysis Using SAS is a comprehensive core text focused on key concepts and techniques in quantitative data analysis using the most current SAS commands and programming language. The coverage of the text is more evenly balanced among statistical analysis, SAS programming, and data/file management than any available text on the market. As such, it provides students with a hands-on, exercise-heavy method for learning basic to intermediate SAS commands while understanding how to apply statistics and reasoning to real-world problems.

Designed to be used in order of teaching preference by instructor, the book is comprised of two sections: the first half of the text instructs students in techniques for data and file managements such as concatenating and merging files, conditional or repetitive processing of variables, and observations. The second half of the text goes into great depth on the most common statistical techniques and concepts - descriptive statistics, correlation, analysis of variance, and regression -áused to analyze data in the social, behavorial, and health sciences using SAS commands.

Data Analysis Using SAS is a complete resource for Data Analysis I and II, Statistics I and II, Quantitative Reasoning, and SAS Programming courses across the social and behavioral sciences and health - especially those that carry a lab component.

 
PART I. INTRODUCTION TO SAS AND BASIC DATA ANALYSIS
 
1. Why do you need to learn SAS for data analyses?
 
2. Where do you start?
 
3. How to prepare data for SAS processing
 
4. From data to a SAS data set
 
5. Enhancing SAS programs and output
 
6. Verifying data
 
7. Data transformation
 
PART II. STATISTICAL PROCEDURES
 
8. Quick descriptive analysis
 
9. Comprehensive descriptive analysis and normality test
 
10.Graphing data
 
11. Categorical data analysis
 
12. T-test of population means
 
13. Analysis of variance
 
14. Inferences about two or more population typical scores by ranks
 
15. Examining trends in data
 
16. Correlation
 
17. When do you stop worrying and start loving regression?
 
PART III. ADVANCED DATA AND FILE MANAGEMENT
 
18. Selecting variables or observations from a SAS data set
 
19. Repetitive and conditional data processing
 
20. Structuring SAS data sets
 
Appendix A. What lies beyond this book? Information on reference books, hotlines, and Internet resources
 
Appendix B. Data sets used in this book
 
Appendix C. Converting SPSS, STATA, Excel, Minitab, Systat data set files to SAS data sets or data set files

“Peng provides an excellent overview of data analysis using the powerful statistical software package SAS. This book is quite appropriate as a self-placed tutorial for researchers, as well as a textbook or supplemental workbook for data analysis courses such as statistics or research methods. Peng provides detailed coverage of SAS capabilities using step-by-step procedures and includes numerous comprehensive graphics and figures, as well as SAS printouts. Readers do not need a background in computer science or programming. Includes numerous examples in education, health sciences, and business.”

D. J. Gougeon
University of Scranton

Shortcomings:

1) I am not quite pleased with the didactic presentation of the data analysis methods considered in book - one needs in any case some other statistical book to understand the methods.
2) I expected more from a book called "Data analysis...". You find there no more advanced data analysis methods than regression.
3) I was articularly looking for a statistical book with accompanied software SAS. This book is in fact about SAS language with some practical applications. Moreover, there is no connection to the SAS Enterprise Guide at all.

The strong point of the book I see in its detailed explanation of SAS language.

So, I would recommend this book for those who has already basic knowledge in statistical methods, for his analysis does not need any non-linear model and want to learn how to implement his model in SAS language.
But I found it not suitable to accompany any (basic or advanced) statistical course.

Dr Tatiana Miazhynskaia
Master Program "Quantitative Asset and Risk Management", University of applied sciences bfi Vienna
November 16, 2009
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Despite offering several affordable packages, we were not allowed to require students to use SAS.

Excellent introduction to SAS and would be an excellent resource.

Mr Brian Kreeger
Business Administration, Metropolitan State University
October 23, 2009
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