Alan Agresti

Statistical Methods for the Social Sciences, Global Edition

• Broschiertes Buch

Produktbeschreibung

For courses in Statistical Methods for the Social Sciences. Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5th Edition uses examples and exercises with a variety of ?real data.? It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics?often a stumbling block for students?while avoiding reliance on an overly simplistic recipe-based approach to statistics.

Produktdetails

• Verlag: Pearson / Pearson Education Limited
• 5th Edition
• Seitenzahl: 568
• Erscheinungstermin: 24. April 2018
• Englisch
• Abmessung: 247mm x 201mm x 30mm
• Gewicht: 1207g
• ISBN-13: 9781292220314
• ISBN-10: 1292220317
• Artikelnr.: 52529056

Autorenporträt

Alan Agresti is a Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He taught statistics there for 38 years, including the development of e-courses in statistical methods for social science students and three courses in categorical data analysis. He is the author of more than 100 refereed articles and six texts, including Statistical Methods for the Social Sciences (Pearson, 5th edition, 2018) and An Introduction to Categorical Data Analysis (Wiley, 3rd edition, 2019). Alan has also received teaching awards from the University of Florida and an Excellence in Writing award from John Wiley & Sons.

Inhaltsangabe

Preface Acknowledgments
1. Introduction
2. Sampling and Measurement
3. Descriptive Statistics
4. Probability Distributions
5. Statistical Inference: Estimation
6. Statistical Inference: Significance Tests
7. Comparison of Two Groups
8. Analyzing Association between Categorical Variables
9. Linear Regression and Correlation
10. Introduction to Multivariate Relationships
11. Multiple Regression and Correlation
12. Regression with Categorical Predictors: Analysis of Variance Methods
13. Multiple Regression with Quantitative and Categorical Predictors
14. Model Building with Multiple Regression
15. Logistical Regression: Modeling Categorical Responses
Appendix: R, Stata, SPSS, and SAS for Statistical Analyses Answers to
Select Odd-Numbered Exercises Bibliography Credits Index