"This book provides a concise treatment of statistical regression modelling together with three detailed case studies for undergraduate students and professional master students with limited background in calculus. All sections have been retained from the second edition, with the author clarifying sections and reworking the more challenging topics. The author has added more end-of-chapter exercises and has added coverage of several new topics. All methods are clearly illustrated using datasets that are available in a variety of formats on the book's supplemental website. Detailed instructions for applying the methods are provided for many software packages, including SPSS, Minitab, DataDesk, SAS, JMP, R, Eviews, Stata, Statistica, and Excel.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.The writing is clear and careful, avoiding overly technical language. There is extensive cross-referencing, an exhaustive index, an appendix with all notation and formulas collected together, a mathematics refresher, a list of references, and a glossary of terms. Illustrations, graphs, and computer |