© 1990

Regression Analysis

Theory, Methods and Applications


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

Table of contents

  1. Front Matter
    Pages i-xv
  2. Ashish Sen, Muni Srivastava
    Pages 1-27
  3. Ashish Sen, Muni Srivastava
    Pages 28-59
  4. Ashish Sen, Muni Srivastava
    Pages 60-82
  5. Ashish Sen, Muni Srivastava
    Pages 83-99
  6. Ashish Sen, Muni Srivastava
    Pages 100-110
  7. Ashish Sen, Muni Srivastava
    Pages 111-131
  8. Ashish Sen, Muni Srivastava
    Pages 132-153
  9. Ashish Sen, Muni Srivastava
    Pages 154-179
  10. Ashish Sen, Muni Srivastava
    Pages 180-217
  11. Ashish Sen, Muni Srivastava
    Pages 218-232
  12. Ashish Sen, Muni Srivastava
    Pages 233-252
  13. Ashish Sen, Muni Srivastava
    Pages 253-264
  14. Back Matter
    Pages 265-348

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.


Fitting Random variable Regression analysis Variance best fit correlation derivative distribution normal distribution statistics

Authors and affiliations

  1. 1.College of Architecture, Art, and Urban Planning, School of Urban Planning and PolicyThe University of IllinoisChicagoUSA
  2. 2.Department of StatisticsUniversity of TorontoTorontoCanada

Bibliographic information

  • Book Title Regression Analysis
  • Book Subtitle Theory, Methods and Applications
  • Authors Ashish K. Sen
    Muni S. Srivastava
  • Series Title Springer Texts in Statistics
  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 1990
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Softcover ISBN 978-3-540-97211-2
  • eBook ISBN 978-3-662-25092-1
  • Series ISSN 1431-875X
  • Edition Number 1
  • Number of Pages XV, 348
  • Number of Illustrations 5 b/w illustrations, 0 illustrations in colour
  • Topics Statistical Theory and Methods
  • Buy this book on publisher's site
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