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Data analysis using SAS Enterprise guide
להגדלת הטקסט להקטנת הטקסט- ספר
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.
| כותר |
Data analysis using SAS Enterprise guide / Lawrence S. Meyers, Glenn Gamst, A.J. Guarino. [electronic resource] |
|---|---|
| מוציא לאור |
Cambridge : Cambridge University Press |
| שנה |
2009 |
| הערות |
Title from publisher's bibliographic system (viewed on 05 Oct 2015). Includes bibliographical references and indexes. English |
| הערת תוכן ותקציר |
Cover Half-title Title Copyright Contents Preface Acknowledgments Section I Introducing SAS Enterprise Guide 1 SAS Enterprise Guide Projects 2 Placing Data into SAS Enterprise Guide Projects Section II Performing Analyses and Viewing Output 3 Performing Statistical Analyses in SAS Enterprise Guide 4 Managing and Viewing Output Section III Manipulating Data 5 Sorting Data and Selecting Cases 6 Recoding Existing Variables 7 Computing New Variables Section IV Describing Data 8 Descriptive Statistics 9 Graphing Data 10 Standardizing Variables Based on the Sample Data 11 Standardizing Variables Based on Existing Norms Section V Score Distribution Assumptions 12 Detecting Outliers 13 Assessing Normality 14 Nonlinearly Transforming Variables in Order to Meet Underlying Assumptions Section 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 Section VII Comparing Means: The t Test 20 Independent-Groups t Test 21 Correlated-Samples t Test 22 Single-Sample t Test Section VIII Comparing Means: ANOVA 23 One-Way Between-Subjects ANOVA 24 Two-Way Between-Subjects Design 25 One-Way Within-Subjects ANOVA 26 Two-Way Mixed ANOVA Design Section IX Nonparametric Procedures 27 One-Way Chi-Square 28 Two-Way Chi-Square 29 Nonparametric Between-Subjects One-Way ANOVA Section X Advanced ANOVA Techniques 30 One-Way Between-Subjects Analysis of Covariance 31 One-Way Between-Subjects Multivariate Analysis of Variance Section XI Analysis of Structure 32 Factor Analysis 33 Canonical Correlation Analysis References Author Index Subject Index |
| היקף החומר |
1 online resource (xix, 378 pages) : digital, PDF file(s). |
| שפה |
אנגלית |
| מספר מערכת |
997010718676405171 |
תצוגת MARC
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