# Understanding Regression Analysis

The Second Edition features updated examples and new references to modern software output.

Author: Larry D. Schroeder

Publisher: SAGE Publications

ISBN: 9781506361611

Category: Social Science

Page: 120

View: 731

Understanding Regression Analysis: An Introductory Guide by Larry D. Schroeder, David L. Sjoquist, and Paula E. Stephan presents the fundamentals of regression analysis, from its meaning to uses, in a concise, easy-to-read, and non-technical style. It illustrates how regression coefficients are estimated, interpreted, and used in a variety of settings within the social sciences, business, law, and public policy. Packed with applied examples and using few equations, the book walks readers through elementary material using a verbal, intuitive interpretation of regression coefficients, associated statistics, and hypothesis tests. The Second Edition features updated examples and new references to modern software output.

# Understanding Regression Analysis

Preface This book is intended to introduce readers who know relatively little about statistics and who have only a basic understanding of mathematics to one of the most powerful techniques in all of statistics: regression analysis.

Author: Michael Patrick Allen

Publisher: Springer Science & Business Media

ISBN: 9780585256573

Category: Social Science

Page: 216

View: 896

By assuming it is possible to understand regression analysis without fully comprehending all its underlying proofs and theories, this introduction to the widely used statistical technique is accessible to readers who may have only a rudimentary knowledge of mathematics. Chapters discuss: -descriptive statistics using vector notation and the components of a simple regression model; -the logic of sampling distributions and simple hypothesis testing; -the basic operations of matrix algebra and the properties of the multiple regression model; -testing compound hypotheses and the application of the regression model to the analyses of variance and covariance, and -structural equation models and influence statistics.

# Understanding Regression Analysis

By assuming it is possible to understand regression analysis without fully comprehending all its underlying proofs and theories, this introduction to the widely used statistical technique is accessible to readers who may have only a ...

Author: Michael Patrick Allen

Publisher: Springer Science & Business Media

ISBN: 9780306456480

Category: Social Science

Page: 216

View: 797

By assuming it is possible to understand regression analysis without fully comprehending all its underlying proofs and theories, this introduction to the widely used statistical technique is accessible to readers who may have only a rudimentary knowledge of mathematics. Chapters discuss: -descriptive statistics using vector notation and the components of a simple regression model; -the logic of sampling distributions and simple hypothesis testing; -the basic operations of matrix algebra and the properties of the multiple regression model; -testing compound hypotheses and the application of the regression model to the analyses of variance and covariance, and -structural equation models and influence statistics.

# Understanding Regression Analysis

Providing beginners with a background to the frequently-used technique of linear regression, this text provides a heuristic explanation of the procedures and terms used in regression analysis and has been written at the most elementary ...

Author: Larry D. Schroeder

Publisher: SAGE

ISBN: 0803927584

Category: Social Science

Page: 95

View: 983

Providing beginners with a background to the frequently-used technique of linear regression, this text provides a heuristic explanation of the procedures and terms used in regression analysis and has been written at the most elementary level.

# Applied Regression Analysis

Doing, Interpreting and Reporting Christer Thrane ... Interaction terms in logit and probit models. Economics Letters, 80, 123–129. ... In Best, H. and C. Wolf: The Sage Handbook of Regression Analysis and Causal Inference, pp. 153–171.

Author: Christer Thrane

Publisher: Routledge

ISBN: 9780429813023

Page: 192

View: 581

This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data. Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complemented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. Parts 1 and 2 of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. Part 3 deals with how to practically handle violations of the classical linear regression assumptions, regression modeling for categorical y-variables and instrumental variable (IV) regression. Part 4 puts the various purposes of, or motivations for, regression into the wider context of writing a scholarly report and points to some extensions to related statistical techniques. This book is written primarily for those who need to do regression analysis in practice, and not only to understand how this method works in theory. The book’s accessible approach is recommended for students from across the social sciences.

# Regression Analysis

The book provides a non-theoretical treatment that is accessible to readers with even a limited statistical background. This book describes exactly how regression models are developed and evaluated.

Author: J. Holton Wilson

ISBN: 9781631573866

Page: 194

View: 395

The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book covers essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The book provides a non-theoretical treatment that is accessible to readers with even a limited statistical background. This book describes exactly how regression models are developed and evaluated. The data used in the book are the kind of data managers are faced with in the real world. The book provides instructions and screen shots for using Microsoft Excel to build business/economic regression models. Upon completion, the reader will be able to interpret the output of the regression models and evaluate the models for accuracy and shortcomings.

# Regression Analysis and its Application

PREFACE Regression analysis is considered indispensable as a data analysis technique in a variety of disciplines. ... Although manipulation of vectors and matrices are essential to the understanding of the topics covered in the last ...

Author: Richard F. Gunst

Publisher: Routledge

ISBN: 9781351419291

Category: Mathematics

Page: 424

View: 449

Regression Analysis and Its Application: A Data-Oriented Approach answers the need for researchers and students who would like a better understanding of classical regression analysis. Useful either as a textbook or as a reference source, this book bridges the gap between the purely theoretical coverage of regression analysis and its practical application. The book presents regression analysis in the general context of data analysis. Using a teach-by-example format, it contains ten major data sets along with several smaller ones to illustrate the common characteristics of regression data and properties of statistics that are employed in regression analysis. The book covers model misspecification, residual analysis, multicollinearity, and biased regression estimators. It also focuses on data collection, model assumptions, and the interpretation of parameter estimates. Complete with an extensive bibliography, Regression Analysis and Its Application is suitable for statisticians, graduate and upper-level undergraduate students, and research scientists in biometry, business, ecology, economics, education, engineering, mathematics, physical sciences, psychology, and sociology. In addition, data collection agencies in the government and private sector will benefit from the book.

# Regression Analysis and Linear Models

Berry, W. D. (1993). Understanding regression assumptions. Thousand Oaks, CA: Sage Publications. Bickel, R. (2007). Multilevel analysis for applied research: It's just regression! New York, NY: Guilford Press. Bollen, K. A. (1989).

Author: Richard B. Darlington

Publisher: Guilford Publications

ISBN: 9781462521135

Category: Social Science

Page: 661

View: 671

Ephasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable?s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

# Generalizing the Regression Model

MODELS: THE. PROCESS. OF. EXPLANATION. This chapter is not about statistical models per se—in fact, it is more of a “theory” chapter. It is also an essential chapter that speaks to all of the previous chapters on regression.

Author: Blair Wheaton

Publisher: SAGE Publications

ISBN: 9781506342115

Category: Social Science

Page: 688

View: 570

This comprehensive text introduces regression, the general linear model, structural equation modeling, the hierarchical linear model, growth curve models, panel data, and event history models, and includes discussion of published implementations of each technique showing how it was used to address substantive and interesting research questions. It takes a step-by-step approach in the presentation of each topic, using mathematical derivations where necessary, but primarily emphasizing how the methods involved can be implemented, are used in addressing representative substantive problems than span a number of disciplines, and can be interpreted in words. The book demonstrates the analyses in STATA and SAS. Generalizing the Regression Model provides students with a bridge from the classroom to actual research practice and application. A website for the book at https://edge.sagepub.com/wheaton1e (coming soon!) includes resources for instructors.

# Correlation and Regression Analysis

Historians , by Roderick Floud , and Understanding Quantitative History , by Kirk Jeffrey and Loren Haskins , clearly discuss the basics of numerical reasoning . Historian's Guide to Statistics : Quantitative Analysis and Historical ...

Author: Thomas J. Archdeacon

Publisher: Univ of Wisconsin Press

ISBN: 029913654X

Category: Social Science

Page: 352

View: 251

In Correlation and Regression Analysis: A Historian's Guide Thomas J. Archdeacon provides historians with a practical introduction to the use of correlation and regression analysis. The book concentrates on the kinds of analysis that form the broad range of statistical methods used in the social sciences. It enables historians to understand and to evaluate critically the quantitative analyses that they are likely to encounter in journal literature and monographs reporting research findings in the social sciences. Without attempting to be a text in basic statistics, the book provides enough background information to allow readers to grasp the essentials of correlation and regression. Correlation analysis refers to the measurement of association between or among variables, and regression analysis focuses primarily on the use of linear models to predict changes in the value taken by one variable in terms of changes in the values of a set of explanatory variables. The book also discusses diagnostic methods for identifying shortcomings in regression models, the use of regression to analyze causation, and the application of regression and related procedures to the study of problems containing categorical as well as numerical data. Archdeacon asserts that knowing how statistical procedures are computed can clarify the theoretical structures underlying them and is essential for recognizing the conditions under which their use is appropriate. The book does not shy away from the mathematics of statistical analysis; but Archdeacon presents concepts carefully and explains the operation of equations step by step. Unlike many works in the field, the book does not assume that readers have mathematical training beyond basic algebra and geometry. In the hope of promoting the role of quantitative analysis in his discipline, Archdeacon discusses the theory and methods behind the most important interpretive paradigm for quantitative research in the social sciences. Correlation and Regression Analysis introduces statistical techniques that are indispensable to historians and enhances the presentation of them with practical examples from scholarly works.