TY  - BOOK
AU  - Pardoe,Iain
TI  - Applied regression modeling
SN  - 9781118345047
AV  - QA278.2 
U1  - 519.5/36 23
PY  - 2012///
CY  - Hoboken, NJ
PB  - Wiley
KW  - Regression analysis
KW  - Statistics
KW  - MATHEMATICS
KW  - Probability & Statistics
KW  - Regression Analysis
KW  - bisacsh
KW  - fast
KW  - Electronic books
N1  - Includes index; Includes bibliographical references and index; Front Matter -- Foundations -- Simple Linear Regression -- Multiple Linear Regression -- Regression Model Building I -- Regression Model Building II -- Case Studies -- Extensions -- Appendix A: Computer Software Help -- Appendix B: Critical Values for t-Distributions -- Appendix C: Notation and Formulas -- Appendix D: Mathematics Refresher -- Appendix E: Brief Answers to Selected Problems -- References -- Glossary -- Index; ssh
N2  - "This book offers a practical, concise introduction to regression analysis for upper-level undergraduate students of diverse disciplines including, but not limited to statistics, the social and behavioral sciences, MBA, and vocational studies. The book's overall approach is strongly based on an abundant use of illustrations, examples, case studies, and graphics. It emphasizes major statistical software packages, including SPSS(r), Minitab(r), SAS(r), R, and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), are provided on a specially prepared and maintained author web site. Select software output appears throughout the text. To help readers understand, analyze, and interpret data and make informed decisions in uncertain settings, many of the examples and problems use real-life situations and settings. The book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series and forecasting. New to this edition are more exercises, simplification of tedious topics (such as checking regression assumptions and model building), elimination of repetition, and inclusion of additional topics (such as variable selection methods, further regression diagnostic tests, and autocorrelation tests)"--
UR  - http://dx.doi.org/10.1002/9781118345054
ER  -