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!
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.
BONUS! Hardcover edition contains a 42-page bonus chapter! Other Multivariate Methods Learn regression analysis at a deeper level with guidance written in everyday language! Intuitively understand regression analysis by focusing on concepts and graphs rather than equations. Learn practical tips for modeling your data 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. Progress from a beginner to a skilled practitioner ready for real-world applications! After an overview of how regression works and why to use it, the book covers a range of topics, including specifying and assessing models, practical applications, types of effects, statistical significance, predictions, and an array of problem-solving techniques. Contains practical and analytical guidance. Select the correct type of regression analysis. Specify the best model and assess how well it fits the data. Interpret the results. Understand main effects, interaction effects, and modeling curvature. Use polynomials, data transformations, and weighted least squares. Generate predictions and evaluate their precision. Check the assumptions and resolve issues. Identify and manage unusual observations. Examples of many regression models and scenarios. Access free downloadable datasets so you can work the examples yourself.
While post- and decolonial theorists have thoroughly debunked the idea of historical progress as a Eurocentric, imperialist, and neocolonialist fallacy, many of the most prominent contemporary thinkers associated with the Frankfurt School—Jürgen Habermas, Axel Honneth, and Rainer Forst—have defended ideas of progress, development, and modernity and have even made such ideas central to their normative claims. Can the Frankfurt School's goal of radical social change survive this critique? And what would a decolonized critical theory look like? Amy Allen fractures critical theory from within by dispensing with its progressive reading of history while retaining its notion of progress as a political imperative, so eloquently defended by Adorno. Critical theory, according to Allen, is the best resource we have for achieving emancipatory social goals. In reimagining a decolonized critical theory after the end of progress, she rescues it from oblivion and gives it a future.
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.
The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
Companion Website materials: https://tzkeith.com/ Multiple Regression and Beyond offers a conceptually-oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. This book: • Covers both MR and SEM, while explaining their relevance to one another • Includes path analysis, confirmatory factor analysis, and latent growth modeling • Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises • Extensive use of figures and tables providing examples and illustrating key concepts and techniques New to this edition: • New chapter on mediation, moderation, and common cause • New chapter on the analysis of interactions with latent variables and multilevel SEM • Expanded coverage of advanced SEM techniques in chapters 18 through 22 • International case studies and examples • Updated instructor and student online resources
