The Cobb-Douglas regression, a statistical technique developed to estimate what economists called a 'production function', was introduced in the late 1920s. For several years, only economist Paul Douglas and a few collaborators used the technique, while vigorously defending it against numerous critics. By the 1950s, however, several economists beyond Douglas's circle were using the technique, and by the 1970s, Douglas's regression, and more sophisticated procedures inspired by it, had become standard parts of the empirical economist's toolkit. This volume is the story of the Cobb-Douglas regression from its introduction to its acceptance as general-purpose research tool. The story intersects with the histories of several important empirical research programs in twentieth century economics, and vividly portrays the challenges of empirical economic research during that era. Fundamentally, this work represents a case study of how a controversial, innovative research tool comes to be widely accepted by a community of scholars.

The benefits of regression therapy extend far beyond the clearing of symptoms. Often, the result is healing at all levels—physical, emotional, and spiritual. Mirrors of Time, by Brian Weiss, M.D., allows you to take regression therapy to the next level. Now you can go back through time by recalling past events that may have led to difficulties in the present. Through the process of remembering, symptoms diminish, and a strong sense of relaxation and well-being often emerges. Even past-life memories can be elicited by these exercises, and regular practice will enhance your physical and emotional health and open up spiritual vistas that can bring new meaning to your life. An audio download is included that goes beyond meditation and visualization exercises—it contains the actual regression techniques Dr. Weiss uses with his patients. By reading Mirrors of Time and practicing the exercises on the accompanying audio, you’ll find that you’ll be filled with more peace, joy, and love—and virtually all aspects of your everyday life will benefit!

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author′s website. NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author′s website.

Collects nearly one thousand photographs to present a comprehensive visual document of the twentieth century

Intuitively understand regression analysis by focusing on concepts and graphs rather than equations and formulas. I use everyday language so you can grasp regression at a deeper level. Progress from a beginner to a skilled practitioner. Learn practical tips for performing your analysis and interpreting the results. Feel confident that you're analyzing your data properly and able to trust your results. Know that you can detect and correct problems that arise. Includes access to free downloadable datasets for the examples. Learn the following: How regression works and when to use it. Selecting the correct type of regression analysis. Specifying the best model. Understanding main effects, interaction effects, and modeling curvature. Interpreting the results. Assessing the fit of the model. Generating predictions and evaluating their precision. Checking the assumptions and resolving issues. Examples of different types of regression analyses.

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.

Jason W. Osborne’s Best Practices in Logistic Regression provides students with an accessible, applied approach that communicates logistic regression in clear and concise terms. The book effectively leverages readers’ basic intuitive understanding of simple and multiple regression to guide them into a sophisticated mastery of logistic regression. Osborne’s applied approach offers students and instructors a clear perspective, elucidated through practical and engaging tools that encourage student comprehension.