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 books 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 software output appears throughout. Detailed exercises collected together at the end of each chapter are carefully chosen to illustrate concepts and aid understanding. Brief solutions for half of the exercises are provided in an appendix while complete solutions to all the exercises are available in an Instructors Manual. Supplemental exercises are available on the books website. In addition, Instructor and student resources are available on the books website, including datasets, software information, presentation slides, supplemental exercises, and videos covering particular topics and software demonstrations.

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