application of regression analysis

  • 324 Pages
  • 3.84 MB
  • 4594 Downloads
  • English
by
Allyn and Bacon , Boston
Regression anal
StatementDick R. Wittink.
Classifications
LC ClassificationsQA278.2 .W58 1988
The Physical Object
Paginationx, 324 p. :
ID Numbers
Open LibraryOL2400589M
ISBN 100205112528
LC Control Number87030740

The Application of Regression Analysis by Dick R. Wittink (Author) › Visit Amazon's Dick R. Wittink Page. Find all the books, read about the author, and more. See search results for this application of regression analysis book.

Are you an author. Learn about Author Central. Dick R. Cited by:   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 by: As the title suggests, the book is about regression analysis.

In addition to multivariate least squares, the book covers advanced linear regression topics like ridge regression. The book could be criticized for being dated in that it does not give examples in R or Matlab. However, the material that is Cited by: This book discusses the need to carefully and prudently apply various regression techniques in order to obtain the full benefits.

It also describes some of the techniques developed and used by the authors, presenting their innovative ideas regarding the formulation and estimation of regression decomposition models, hidden Markov chain, and the contribution of regressors in the set-theoretic. User Review - Flag as inappropriate Books Regression Analysis: Theory, Methods, and Applications Ashish Sen, Muni Srivastava - Psychology - - pages An up-to-date, rigorous, and lucid treatment of the theory, methods, and applications of regression analysis, and thus ideally suited for those interested in the theory as well as those whose interests lie primarily with applications.5/5(2).

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.

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 application of regression analysis book equations).

The writing of this book 5/5(1). "[This book] provides a good balance of relevant theory and application with many examples [and it] provides the most balanced approach to theory and application appropriate for a first course in nonlinear regression modeling for graduate statistics students." —Mathematical Reviews.

More than new exercises in the areas of regression and analysis of variance As in the First Edition, the author presents a thorough treatment of the concepts and methods of linear model analysis, and illustrates them with various numerical and conceptual examples, using a data-based approach to development and analysis.

The book begins with a comprehensive introduction to linear models and continues on to cover the most useful advanced topics, such as logistic regression and mediation and path analysis.

A 'must-have' desk reference for entry-level learners and long-time practitioners alike."--Elizabeth Page-Gould, PhD, Canada Research Chair in Social /5(11). Book PDF Available. 7 F urther Applications of Regression T echniques Regression analysis is a collection of statistical techniques that serve as a basis for draw.

Springer Texts in Statistics Alfred: Elements of Statistics for the Life and Social Sciences Berger: An Introduction to Probability and Stochastic Processes Bilodeau and Brenner: Theory of Multivariate Statistics Blom: Probability and Statistics: Theory and Applications Brockwell and Davis: An Introduction to Times Series and Forecasting Chow and Teicher: Probability Theory: Independence.

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 author's website () provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. how to apply correlation and regression statistical data analysis techniques to investigate the variables affecting phenomenon of employment and unemployment.

There are many terms that need introduction before we get started with the Size: 1MB. Taking into consideration current statistical technology, Introductory Regression Analysis focuses on the use and interpretation of software, while also demonstrating the logic, reasoning, and calculations that lie behind any statistical analysis.

Furthermore, the text emphasizes the application of regression tools to real-life business by: Regression Analysis and Linear Models: Concepts, Applications, and Implementation - Ebook written by Richard B. Darlington, Andrew F. Hayes.

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Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Regression Analysis and Linear Models: Concepts, Applications, and Implementation.

doing regression analysis. Data analysis is perhaps an art, and certainly a craft; we believe that the goal of any data analysis book should be to help analysts develop the skills and experience necessary to adjust to the inevitable twists and turns that come up when analyzing real data.

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Get Books The essentials of regression analysis through practical applications Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement.

User Review - Flag as inappropriate Regression Analysis by Example Samprit Chatterjee, Ali S. Hadi - Mathematics - - pages The essentials of regression analysis through practical applications Regression analysis is a conceptually simple method for investigating relationships among variables.

Carrying out a successful application of regression analysis, however, requires a balance of 4/5(3). Book is unique both for its coverage and writing style. Re writing style, readers indicate that it is like sitting in a living room in front of a fire chatting about statistics with the author. Re coverage, the book has an applications focus, so there are many applied examples of how to use and consider the techniques in unique ways.

Regression Analysis: Concepts and Applications focuses on thinking clearly about and solving practical statistical problems.

The approach leads from the theoretical (meaning conceptual not mathematical) to the applied, the idea being that samples (using theory) tell the investigator what needs to be known about populations (for application).

on application and implementation using statistical software. Although our intention was to deliver an introductory treatment of regression analysis theory and application, we think even the seasoned researcher and user of regression analysis will find him- or herself learn-ing something new in each chapter.

Indeed, with repeated readings of this. Here some applications of regression analysis in business. Uses of Regression Analysis in Business 5.

Predictive analytics i.e. forecasting future opportunities and risks is the most prominent application of regression analysis in business. Demand analysis, for.

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Springer Texts in Business and Economics, DOI /_7, # Springer-Verlag Berlin Heidelberg regression, correlation, significance tests, and simple analysis of variance. Our hope is that researchers and students with such a background will find this book a relatively self-contained means of using SPSS to analyze their data correctly.

Each chapter ends with a. This text is designed to help students fully understand regression analysis, its components, and its uses. Taking into consideration current statistical technology, it focuses on the use and interpretation of software, while also demonstrating the logic, reasoning, and calculations that lie behind any statistical analysis.5/5(1).

Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis Frank E. Harrell, Jr. (auth.) This highly anticipated second edition features new chapters and sections, new references, and comprehensive R software.

Introduction to Correlation and Regression Analysis. In this section we will first discuss correlation analysis, which is used to quantify the association between two continuous variables (e.g., between an independent and a dependent variable or between two independent variables). This book is composed of four chapters covering a variety of topics about using Stata for regression.

We should emphasize that this book is about “data analysis” and that it demonstrates how Stata can be used for regression analysis, as opposed to a book that covers the statistical basis of multiple regression.

Applied Logistic Regression Analysis; Interaction Effects in Logistic Regression; Learn About Logistic Regression in R With Data From the American National Election Study ; Learn About Logistic Regression in R With Data From the Behavioral Risk Factor Surveillance System ().

An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume.Book Description.

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.Results of binary logistic regression of ¯ight pro®les analysis and applications, book will assist in improving the practitioner's knowledge of the methods so that they.