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Regression Analysis

Theory, Methods and Applications

  • Book
  • © 1990

Overview

Authors:
  1. Ashish Sen

    1. College of Architecture, Art, and Urban Planning, School of Urban Planning and Policy, The University of Illinois, Chicago, USA

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  2. Muni Srivastava

    1. Department of Statistics, University of Toronto, Toronto, Canada

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: Springer Texts in Statistics (STS)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xv
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  2. Introduction

    • Ashish Sen, Muni Srivastava
    Pages 1-27

  3. Multiple Regression

    • Ashish Sen, Muni Srivastava
    Pages 28-59

  4. Tests and Confidence Regions

    • Ashish Sen, Muni Srivastava
    Pages 60-82

  5. Indicator Variables

    • Ashish Sen, Muni Srivastava
    Pages 83-99

  6. The Normality Assumption

    • Ashish Sen, Muni Srivastava
    Pages 100-110

  7. Unequal Variances

    • Ashish Sen, Muni Srivastava
    Pages 111-131

  8. Correlated Errors

    • Ashish Sen, Muni Srivastava
    Pages 132-153

  9. Outliers and Influential Observations

    • Ashish Sen, Muni Srivastava
    Pages 154-179

  10. Transformations

    • Ashish Sen, Muni Srivastava
    Pages 180-217

  11. Multicollinearity

    • Ashish Sen, Muni Srivastava
    Pages 218-232

  12. Variable Selection

    • Ashish Sen, Muni Srivastava
    Pages 233-252

  13. Biased Estimation

    • Ashish Sen, Muni Srivastava
    Pages 253-264

  14. Back Matter

    Pages 265-348
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Keywords

About this book

Any method of fitting equations to data may be called regression. Such equations are valuable for at least two purposes: making predictions and judging the strength of relationships. Because they provide a way of em­ pirically identifying how a variable is affected by other variables, regression methods have become essential in a wide range of fields, including the soeial seiences, engineering, medical research and business. Of the various methods of performing regression, least squares is the most widely used. 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 of equations). The writing of this book started in 1982. Since then, various drafts have been used at the University of Toronto for teaching a semester-Iong course to juniors, seniors and graduate students in a number of fields, including statistics, pharmacology, pharmacology, engineering, economics, forestry and the behav­ ioral seiences. Parts of the book have also been used in a quarter-Iong course given to Master's and Ph.D. students in public administration, urban plan­ ning and engineering at the University of Illinois at Chicago (UIC). This experience and the comments and critieisms from students helped forge the final version.

Authors and Affiliations

  • College of Architecture, Art, and Urban Planning, School of Urban Planning and Policy, The University of Illinois, Chicago, USA

    Ashish Sen

  • Department of Statistics, University of Toronto, Toronto, Canada

    Muni Srivastava

Bibliographic Information

  • Book Title: Regression Analysis

  • Book Subtitle: Theory, Methods and Applications

  • Authors: Ashish Sen, Muni Srivastava

  • Series Title: Springer Texts in Statistics

  • DOI: https://doi.org/10.1007/978-3-662-25092-1

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1990

  • Softcover ISBN: 978-3-540-97211-2Published: 01 January 1994

  • eBook ISBN: 978-3-662-25092-1Published: 11 November 2013

  • Series ISSN: 1431-875X

  • Series E-ISSN: 2197-4136

  • Edition Number: 1

  • Number of Pages: XV, 348

  • Number of Illustrations: 5 b/w illustrations

  • Topics: Statistical Theory and Methods, Analysis

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