Introduction to linear regression analysis pdf montgomery

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introduction to linear regression analysis pdf montgomery

Introduction to Linear Regression Analysis, 5th Edition [Book]

You are currently using the site but have requested a page in the site. Would you like to change to the site? Douglas C. Montgomery , Elizabeth A. Peck , G. Geoffrey Vining. A comprehensive and up-to-date introduction to the fundamentals of regression analysis.
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Linear Regression - Machine Learning Fun and Easy

Introduction to Linear Regression Analysis, 5th Edition

Please Read Notes: Brand New, International Softcover Edition, Printed in black and white pages, minor self wear on the cover or pages, Sale restriction may be printed on the book, but Book name, contents, and author are exactly same as Hardcover Edition. Convert currency. Add to Basket. Book Description Wiley. Condition: NEW. End Chapter Exercises may differ. Legal to use despite any disclaimer.

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Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today's cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including:. A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models.


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