Applied Regression Modeling offers a practical, concise introduction to regression analysis for upper-level undergraduate or graduate students of diverse disciplines including, but not limited to statistics, the social and behavioural sciences, business, and vocational studies. The book’s overall approach is strongly based on an abundant use of illustrations, examples, graphics, and case studies. It develops regression methods in an intuitive fashion so that a strong mathematical background is not needed to master the material (more extensive mathematical details and formulas are provided in optional sections). It emphasizes major statistical software packages, including SPSS, Minitab, SAS, JMP, Data Desk, EViews, Stata, Statistica, R, and Python. Detailed instructions for use of these packages 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, and Bayesian modeling. New to this edition are more exercises, updated examples, chapter learning objectives, clarification and expansion of challenging topics (such as assessing model assumptions, analysis of variance, sums of squares, lack of fit testing, hierarchical models, influential observations, weighted least squares, multicollinearity, and logistic regression), and new material on matrices in the context of multiple linear regression. This accompanying website adds new instructional videos and practice quizzes.