Applied Regression Modeling

The 3rd Edition of Applied Regression Modeling is a concise but comprehensive treatment of statistical regression analysis for those with little or no background in calculus. It covers a wide variety of topics, including simple linear regression models, multiple linear regression, regression model building, and three fully described case studies. It is perfect for students of any undergraduate statistics course and non-statistics graduate students.

Format: Hardback
Length: 336 pages
Publication date: 22 January 2021
Publisher: John Wiley and Sons Ltd

Master the fundamentals of regression without learning calculus with this one-stop resource! The newly and thoroughly revised 3rd Edition of Applied Regression Modeling delivers a concise but comprehensive treatment of the application of statistical regression analysis for those with little or no background in calculus. Accomplished instructor and author Dr. Iain Pardoe has reworked many of the more challenging topics, included learning outcomes and additional end-of-chapter exercises, and added coverage of several brand-new topics, including multiple linear regression using matrices.

The methods described in the text are clearly illustrated with multi-format datasets available on the book's supplementary website. In addition to a fulsome explanation of foundational regression techniques, the book introduces modeling extensions that illustrate advanced regression strategies, including model building, logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series forecasting. Illustrations, graphs, and computer software output appear throughout the book to assist readers in understanding and retaining the more complex content.

Applied Regression Modeling covers a wide variety of topics, like:

Simple linear regression models, including the least squares criterion, how to evaluate model fit, and estimation/prediction.

Multiple linear regression, including testing regression parameters, checking model assumptions graphically, and testing model assumptions numerically.

Regression model building, including predictor and response variable transformations, qualitative predictors, and regression pitfalls.

Three fully described case studies, including one each on home prices, vehicle fuel efficiency, and pharmaceutical patch.

Whether you're a business analyst, data scientist, or researcher, Applied Regression Modeling provides the essential tools and techniques you need to understand and apply regression analysis in your work. Don't let calculus hold you back – master the fundamentals of regression with this comprehensive and accessible guide!

Weight: 762g
Dimension: 185 x 263 x 27 (mm)
ISBN-13: 9781119615866
Edition number: 3rd Edition