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Download Introduction to Linear Regression Analysis, 2nd Edition eBook

by Douglas C. Montgomery,Elizabeth A. Peck

Download Introduction to Linear Regression Analysis, 2nd Edition eBook
ISBN:
0471533874
Author:
Douglas C. Montgomery,Elizabeth A. Peck
Category:
Mathematics
Language:
English
Publisher:
Wiley-Interscience; 2 edition (February 19, 1992)
Pages:
544 pages
EPUB book:
1713 kb
FB2 book:
1520 kb
DJVU:
1856 kb
Other formats
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Rating:
4.3
Votes:
172


REGRESSION ANALYSIS OF TIME SERIES DATA 474 1. Introduction to Regression Models for Time Series . This book is intended as a text for a basic course in regression analysis. It contains the standard topics for such courses and many of the newer ones as well.

REGRESSION ANALYSIS OF TIME SERIES DATA 474 1. Introduction to Regression Models for Time Series Data, 474 1. Detecting Autocorrelation: The Durbin-Watson Test, 475 1. Estimating the Parameters in Time Series Regression Models, 480 Problems, 496 1. It blends both theory and application so that the reader will gain an understanding of the basic principles necessary to apply regression model-building techniques in a wide variety of application environments.

Read instantly in your browser. by Douglas C. Montgomery (Author), Elizabeth A. Peck (Author). ISBN-13: 978-0471533870. The 13-digit and 10-digit formats both work.

Introduction to linear regression analysis, Douglas C. Montgomery, Elizabeth A. Peck, . Introd. Applied Statistics and Probability for Engineers. Douglas C. Montgomery-Design and Analysis of Experiments-Wiley. 81 MB·3,004 Downloads. 97 MB·1,814 Downloads·New! Applied Statistics and Probability for Engineers provides a practical approach to probability.

Introduction to nonlinear regression 1. linear and nonlinear regression is a. . LINEAR AND NONLINEAR REGRESSION is a member of the exponential family. Regression analysis of time series data 1. introduction to regression models for time series data 1. detecting autocorrelation: the durbinwatson test 1. estimating the parameters in time series regression models problems chapter 15.

Douglas C. Montgomery. Introduction to Correlation and Linear Regression Analysis. Linear regression analysis could get better results in a short-term forecast. Introduction to Regression Analysis. However, when some aberrant points exist in a given raw data sequence, it will be difficult for the linear regression function to accurately predict the changing tendency of the data sequence. To solve the problem, firstly, the raw data sequence with some abnormal data is classified into two parts: aberrant data and.

Introduction to Linear Regression Analysis by. Montgomery, G. Geoffrey Vining. Montgomery’s most popular book is Design and Analysis of Experiments. Showing 30 distinct works. Design and Analysis of Experiments by. Introduction to Linear Regression Analysis by.

Appendix E. Introduction to R to Perform Linear Regression Analysis 623.

ELIZABETH A. PECK, PHD, is Logistics Modeling Specialist at the Coca-Cola Company in Atlanta, Georgia. G. GEOFFREY VINING, PHD, is Professor in the Department of Statistics at Virginia Polytechnic and State University. Appendix E.

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.

Covers both theory and application so the reader can understand the basic principles and apply regression methods in a variety of practical settings. Revisions include new material on regression diagnostics, more sample computer output with expanded interpretations, a discussion on handling missing observations and introductions to handling generalized linear models and nonlinear regression.
  • Stonewing
This is a higher level introduction and somewhat theoretical approach to linear regression analysis. It is well written. However, one should have had multivariable calculus and calculus based probability to get the most out of this book.
  • Gtonydne
good