» » Applied Regression Modeling: A Business Approach
Download Applied Regression Modeling: A Business Approach epub book
ISBN:0471970336
Author: Iain Pardoe
ISBN13: 978-0471970330
Title: Applied Regression Modeling: A Business Approach
Format: txt mobi lit docx
ePUB size: 1759 kb
FB2 size: 1317 kb
DJVU size: 1940 kb
Language: English
Category: Mathematics
Publisher: Wiley-Interscience; 1 edition (July 21, 2006)
Pages: 324

Applied Regression Modeling: A Business Approach by Iain Pardoe



Welcome to the homepage of the textbook Applied Regression Modeling: A Business Approach by Iain Pardoe, published by Wiley in 2006 (ISBN 978-0-471-97033-0).

An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculus Regression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression.

Applied Regression Modeling: A Business Approach, Iain Pardoe," The American Statistician, American Statistical Association, vol. 61, pages 367-368, November. Handle: RePEc:bes:amstat:v:61:y:2007:m:november:p:367-368.

Only 5 left in stock (more on the way).

Personal Name: Pardoe, Iain, 1970-. Publication, Distribution, et. Hoboken, . Wiley-Interscience, (c)2006. On this site it is impossible to download the book, read the book online or get the contents of a book. The administration of the site is not responsible for the content of the site. The data of catalog based on open source database. All rights are reserved by their owners. Download book Applied regression modeling : a business approach, Iain Pardoe.

Magazine: Applied Regression Modeling: A Business Approach. Save as template? Title.

Start by marking SAS System for Regression + Applied Regression Modeling: A Business Approach Package as Want to Read: Want to Read savin. ant to Read. Be the first to ask a question about SAS System for Regression + Applied Regression Modeling. Lists with This Book. This book is not yet featured on Listopia.

Applied Regression Modeling. The book is very well written and the author is extremely careful with his descriptions. the examples are wonderful.

An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculus Regression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression analysis to make informed decisions. Applied Regression Modeling: A Business Approach offers a practical, workable introduction to regression analysis for upper-level undergraduate business students, MBA students, and business managers, including auditors, financial analysts, retailers, economists, production managers, and professionals in manufacturing firms. The book's overall approach is strongly based on an abundant use of illustrations and graphics and uses major statistical software packages, including SPSS(r), Minitab(r), SAS(r), and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), are provided, although Excel does not have a built-in capability to carry out all the techniques discussed. Applied Regression Modeling: A Business Approach offers special user features, including: * A companion Web site with all the datasets used in the book, classroom presentation slides for instructors, additional problems and ideas for organizing class time around the material in the book, and supplementary instructions for popular statistical software packages. An Instructor's Solutions Manual is also available. * A generous selection of problems-many requiring computer work-in each chapter with fullyworked-out solutions * Two real-life dataset applications used repeatedly in examples throughout the book to familiarize the reader with these applications and the techniques they illustrate * A chapter containing two extended case studies to show the direct applicability of the material * A chapter on modeling extensions illustrating more advanced regression techniques through the use of real-life examples and covering topics not normally seen in a textbook of this nature * More than 100 figures to aid understanding of the material Applied Regression Modeling: A Business Approach fully prepares professionals and students to apply statistical methods in their decision-making, using primarily regression analysis and modeling. To help readers understand, analyze, and interpret business data and make informed decisions in uncertain settings, many of the examples and problems use real-life data with a business focus, such as production costs, sales figures, stock prices, economic indicators, and salaries. A calculus background is not required to understand and apply the methods in the book.
Reviews: 4
Shaktit
Iain Pardoe targets his introduction to regression analysis at undergraduate students who have already taken a basic statistics class and non statistics-major graduate students. His focus is data modeling, not formula derivation nor computational detail.

The book covers a lot in seven chapters. The first two chapters are standard fare, reviewing basic concepts of hypothesis testing and introducing simple linear regression. Then the book breaks new ground. The third chapter on multiple regression revisits some of the simple regression examples, examining them in greater depth and demonstrating the value of including more than one predictor. It also extendes the earlier chapter's problem-solving approach, encouraging readers to think about data and the best way to understand it. By the end of the fifth chapter, readers understand how to interpret output from statisitcal software, transform variables, and diagnose violations of statistical assumptions. Chapter 5 ends with a "Model Building Guidelines" section that shows how to apply the book's lessons more generally. The sixth chapter of case studies exercises these skills and a final chapter on extensions of regression points to further reading.

This is one of the best-written statistics textbooks I have encountered, as a student or instructor. The work-and-reflect style captures the feel of learning from experience. The author's thoughtful selection of related data sets, problems, and examples compresses that experience into an efficient, short-course form. There is very little derivation of formulas. Instead the focus is on choosing proceedures, interpreting stat package output, and making decisions about how to model data.

The integration with statistical software has been done particularly well. Most books of this type fall into one of three traps. If they focus on one stat package the examples are more accessible, but the book includes too much "technical support" and only speaks well to users of one package. A wider audience can be reached by including only examples workable with basic calculator functions. But such examples are simplistic and the reader's focus is on in-the-weeds computational details. Some books try for middle ground by including their own software. I have yet to see such software that is easy to use--and that scales up to real analysis problems.

Pardoe has found an elegant solution. He supports five software packages (SPSS, Minitab, SAS, R/S-PLUS, and Excel. A .zip file on the books web set contains properly-formatted data files for each package. Each package also has it's own appendix of numbered "Computer Helps" explaining the statistical commands required for specific analyses. Within the text there are no software-specific directions, just parenthetic notes such as "See Computer Help #14". Readers consult the numbered section in the appendix for their software. This is simple, supports multiple software packages, and keeps the text's focus on modeling rather than minutia. Nicely done!

This is an excellent textbook on regression, perfect for readers with real research problems to solve. It is highly recommended.
TheSuspect
There are so many great things about this book on applied regression that it is hard to know where to start. I'll mention six areas. First, the text teaches from the standpoint of someone learning rather than just presenting topics. The text goes carefully through examples that draw attention to what works and what does not work. Second, I like the chapter on extensions of regression. Third, I like the detailed steps of each hypothesis test along with the meaning of each step. Fourth, I like the thorough review of transformations of predictors and transformations of the dependent variable. Fifth, great treatment of categorical values. Sixth: good use of graphs to study whether the four assumptions of regression are met. Bottom line: if you study this book carefully and work through the examples, you will have a useful tool box to apply to a wide variety of business problems. If you are interested in this topic, I highly recommend this text. It's well worth the investment.
Folsa
This is an awesome book, teaches you regression step by step very practical. Has numerous exercises to practice, it is also very well written. If you are looking for one single volume covering fundamentals of regression this is your book. It does not need a strong math background to follow and understand which makes it useful for a wide variety of readers.
Kikora
This book is confusing and dense. I wish I hadn't purchased it. As someone who is certainly NOT an expert in statistics, this book did little to help me understand the subject. Perhaps someone better versed in stats would like it better.