Data Analysis Using SAS Enterprise Guide-经管之家官网！

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.

I Introducing SAS Enterprise Guide

1 SAS Enterprise Guide Projects 3

2 Placing Data into SAS Enterprise Guide Projects 13

II Performing Analyses and Viewing Output

3 Performing Statistical Analyses in SAS Enterprise Guide 25

4 Managing and Viewing Output 32III Manipulating Data

5 Sorting Data and Selecting Cases 43

6 Recoding Existing Variables 53

7 Computing New Variables 63

IV Describing Data

8 Descriptive Statistics 77

9 Graphing Data 91

10 Standardizing Variables Based on the Sample Data 104

11 Standardizing Variables Based on Existing Norms 111

V Score Distribution Assumptions

12 Detecting Outliers 119

13 Assessing Normality 130

VI Correlation and Prediction

14 Nonlinearly Transforming Variables in Order to Meet Underlying Assumptions 135

15 Bivariate Correlation: Pearson Product–Moment and Spearman Rho Correlations 155

16 Simple Linear Regression 162

17 Multiple Linear Regression 170

18 Simple Logistic Regression 177

19 Multiple Logistic Regression 185

VII Comparing Means: ThetTest

20 Independent-GroupstTest 195

21 Correlated-SamplestTest 201

22 Single-SampletTest 206

VIII Comparing Means: ANOVA

23 One-Way Between-Subjects ANOVA 213

24 Two-Way Between-Subjects Design 223

25 One-Way Within-Subjects ANOVA 238

26 Two-Way Mixed ANOVA Design 253

IX Nonparametric Procedures

27 One-Way Chi-Square 269

28 Two-Way Chi-Square 279

29 Nonparametric Between-Subjects One-Way ANOVA 291

X Advanced ANOVA Techniques

30 One-Way Between-Subjects Analysis of Covariance 299

31 One-Way Between-Subjects Multivariate Analysis of Variance 313

XI Analysis of Structure

32 Factor Analysis 327

33 Canonical Correlation Analysis 345

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