Introduction to Linear Regression Analysis + Solutions Manual

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Introduction to Linear Regression Analysis + Solutions Manual
می 8, 2019
61 مگابایت
15000 تومان
15000 تومان – خرید

Introduction to Linear Regression Analysis + Solutions Manual

Introduction to Linear Regression Analysis + Solutions Manual

تحلیل رگرسیون خطی مونتگومری + راهنمای حل مسائل

زبان: انگلیسی

نویسنده:  Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining

ناشر: Wiley

سال انتشار: ۲۰۱۳

ویرایش: پنجم


A comprehensive and up-to-date introduction to the fundamentals of regression analysis

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 auto correlation as well as parameter estimation in time series regression models
  • Regression models with random effects in addition to a discussion on sub sampling and the importance of the mixed model
  • Tests on individual regression coefficients and subsets of coefficients
  • Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data.

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