Running Regressions
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Running Regressions
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Author : Michelle C. Baddeley
language : en
Publisher: Cambridge University Press
Release Date : 2009-05-28
Running Regressions written by Michelle C. Baddeley and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-28 with Business & Economics categories.
Running Regressions introduces first-year social science undergraduates, particularly those studying economics and business, to the practical aspects of simple regression analysis, without adopting an esoteric, mathematical approach. It shows that statistical analysis can be simultaneously straightforward, useful and interesting, and can deal with topical, real-world issues. Each chapter introduces an economic theory or idea by relating it to an issue of topical interest, and explains how data and econometric analysis can be used to test it. The book can be used as a self-standing text or to supplement conventional econometric texts. It is also ideally suited as a guide to essays and project work.
Modeling Nonlinearity And Interaction In Regression Analysis Using Spline Variables
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Author : Roger A. Wojtkiewicz
language : en
Publisher: SAGE Publications
Release Date : 2025-05-22
Modeling Nonlinearity And Interaction In Regression Analysis Using Spline Variables written by Roger A. Wojtkiewicz and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-22 with Social Science categories.
"Spline variables and their interactions play a crucial role in the field of social science. This book offers a comprehensive and detailed exploration of this method, providing valuable insights and information for researchers in the field." --Man-Kit Lei, The University of Georgia This volume addresses the issue of linear constraints in regression modeling. Author Roger A. Wojtkiewicz uses the method of knotted spline variables (also known as piecewise linear regression) and a new method involving group spline variables to model nonlinearity in a variety of situations. Using spline variables to model nonlinearity allows researchers to specify unrestricted models for models that involve interval variables, allowing for greater flexibility in modeling any possible interaction.
Business Analysis Using Regression
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Author : Robert A. Stine
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Business Analysis Using Regression written by Robert A. Stine and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
Preface Statistics is seldom the most eagerly anticipated course of a business student. It typically has the reputation ofbeing aboring, complicated, and confusing mix of mathematical formulas and computers. Our goal in writing this casebook and the companion volume (Basic Business Statistics) was to change that impression by showing how statistics gives insights and answers interesting business questions. Rather than dwell on underlying formulas, we show how to use statistics to answer questions. Each case study begins with a business question and concludes with an answer. Formulas appear only as needed to address the questions, and we focus on the insights into the problem provided by the mathematics. The mathematics serves a purpose. The material is organized into 12 "classes" of related case studies that develop a single, key idea of statistics. The analysis of data using statistics is seldom very straightforward, and each analysis has many nuances. Part ofthe appeal ofstatistics is this richness, this blending of substantive theories and mathematics. For a newcomer, however, this blend is too rich and they are easily overwhelmed and unable to sort out the important ideas from nuances. Although later cases in these notes suggest this complexity, we do not begin that way. Each class has one main idea, something big like standard error. We begin a class by discussing an application chosen to motivate this key concept, and introduce the necessary terminology.
The Sage Handbook Of Regression Analysis And Causal Inference
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Author : Henning Best
language : en
Publisher: SAGE
Release Date : 2013-12-20
The Sage Handbook Of Regression Analysis And Causal Inference written by Henning Best and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-20 with Social Science categories.
′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.
Valuing Businesses Using Regression Analysis
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Author : C. Fred Hall, III
language : en
Publisher: John Wiley & Sons
Release Date : 2021-04-16
Valuing Businesses Using Regression Analysis written by C. Fred Hall, III and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-16 with Business & Economics categories.
Demystifies regression-based valuation through simple explanations, easy-to-understand charts, and time-saving bonus resources Current methodologies using median, quartiles, or standard deviations to calculate revenue multipliers and cash flow multipliers often produce values that are wildly divergent. This forces the appraiser to choose between a very high or a very low value or consider averaging the values, opening the possibility for the appraiser to be challenged. On the surface, regression analysis appears to be the more complex, mathematical model, so many professionals shy away from using it out of fear that readers will be confused and reject the results of the appraisal. Valuing Businesses Using Regression Analysis solves this issue by breaking down regression to its simplest terms and providing easy-to-read charts and explanations that can be understood by all. Since regression analysis does not come pre-installed in Excel, this book will show you how to enable Excel's regression in your computer. Then you'll learn four different Regression tools that can be used for business valuations or for forecasting in general. As an added perk, this book also comes with a template that simplifies the entire regression methodology into the click of one button. With a minimal amount of work, you can use this template to produce a compelling four-page valuation report. See why current valuation methodologies can be wildly inaccurate and why regression analysis is a practical and preferable alternative Learn how to set up Regression in Excel and use single-variable linear regression to predict revenue and cash flow multipliers Walk through the process for conducting more advanced analyses, including curved regression with outliers and multiple variable regression Use the bonus template to create attractive four-page valuation reports using regression analysis in Excel This book is an excellent choice for valuators and other financial professionals ready to take the leap into regression analysis for more accurate, more objective business valuations.
Regression Analysis
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Author : Ashish K. Sen
language : en
Publisher: Springer
Release Date : 2013-11-11
Regression Analysis written by Ashish K. Sen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.
Any method of fitting equations to data may be called regression. Such equations are valuable for at least two purposes: making predictions and judging the strength of relationships. Because they provide a way of em pirically identifying how a variable is affected by other variables, regression methods have become essential in a wide range of fields, including the soeial seiences, engineering, medical research and business. Of the various methods of performing regression, least squares is the most widely used. In fact, linear least squares regression is by far the most widely used of any statistical technique. Although nonlinear least squares is covered in an appendix, this book is mainly ab out linear least squares applied to fit a single equation (as opposed to a system of equations). The writing of this book started in 1982. Since then, various drafts have been used at the University of Toronto for teaching a semester-Iong course to juniors, seniors and graduate students in a number of fields, including statistics, pharmacology, pharmacology, engineering, economics, forestry and the behav ioral seiences. Parts of the book have also been used in a quarter-Iong course given to Master's and Ph.D. students in public administration, urban plan ning and engineering at the University of Illinois at Chicago (UIC). This experience and the comments and critieisms from students helped forge the final version.
Regression Analysis
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Author : Ashish Sen
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Regression Analysis written by Ashish Sen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Psychology categories.
Any method of fitting equations to data may be called regression. Such equations are valuable for at least two purposes: making predictions and judging the strength of relationships. Because they provide a way of em pirically identifying how a variable is affected by other variables, regression methods have become essential in a wide range of fields, including the social sciences, engineering, medical research and business. Of the various methods of performing regression, least squares is the most widely used. In fact, linear least squares regression is by far the most widely used of any statistical technique. Although nonlinear least squares is covered in an appendix, this book is mainly about linear least squares applied to fit a single equation (as opposed to a system of equations). The writing of this book started in 1982. Since then, various drafts have been used at the University of Toronto for teaching a semester-long course to juniors, seniors and graduate students in a number of fields, including statistics, pharmacology, engineering, economics, forestry and the behav ioral sciences. Parts of the book have also been used in a quarter-long course given to Master's and Ph.D. students in public administration, urban plan ning and engineering at the University of Illinois at Chicago (UIC). This experience and the comments and criticisms from students helped forge the final version.
Modern Regression Techniques Using R
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Author : Daniel B Wright
language : en
Publisher: SAGE
Release Date : 2009-02-19
Modern Regression Techniques Using R written by Daniel B Wright and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-19 with Mathematics categories.
Statistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language. Statistical and computing advances have led to new and exciting regressions that have become the necessary tools for any researcher in these fields. In a way that is refreshingly engaging and readable, Wright and London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books′ webpage: www.sagepub.co.uk/wrightandlondon. Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter. Given the wide coverage of techniques, this book will be essential reading for any advanced undergraduate and graduate student (particularly in psychology) and for more experienced researchers wanting to learn how to apply some of the more recent statistical techniques to their datasets. The Authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.
Data Analysis Using Regression And Multilevel Hierarchical Models
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Author : Andrew Gelman
language : en
Publisher: Cambridge University Press
Release Date : 2007
Data Analysis Using Regression And Multilevel Hierarchical Models written by Andrew Gelman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Mathematics categories.
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Predictions In Time Series Using Regression Models
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Author : Cory Terrell
language : en
Publisher: Scientific e-Resources
Release Date : 2019-09-02
Predictions In Time Series Using Regression Models written by Cory Terrell and has been published by Scientific e-Resources this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-02 with categories.
Regression methods have been a necessary piece of time arrangement investigation for over a century. As of late, new advancements have made real walks in such territories as non-constant information where a direct model isn't fitting. This book acquaints the peruser with fresher improvements and more assorted regression models and methods for time arrangement examination. Open to any individual who knows about the fundamental present day ideas of factual deduction, Regression Models for Time Series Analysis gives a truly necessary examination of late measurable advancements. Essential among them is the imperative class of models known as summed up straight models (GLM) which gives, under a few conditions, a bound together regression hypothesis reasonable for constant, all out, and check information. The creators stretch out GLM methodology deliberately to time arrangement where the essential and covariate information are both arbitrary and stochastically reliant. They acquaint readers with different regression models created amid the most recent thirty years or somewhere in the vicinity and condense traditional and later outcomes concerning state space models.