Regression Analysis

1. Front Matter

   Pages i-xv

   PDF

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 265-347

    PDF

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 social sciences, 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 about 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-long course to juniors, seniors and graduate students in a number of fields, including statistics, pharmacology, engineering, economics, forestry and the behav­ ioral sciences. Parts of the book have also been used in a quarter-long 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 criticisms from students helped forge the final version.

• Bootstrapping
• Estimator
• Measure
• Normal distribution
• Random variable
• Regression analysis
• best fit

"I found this to be the most complete and up-to-date regression text I have come across...this text has much to offer."
-Journal of the American Statistical
Association
"The material is presented in a lucid and easy-to-understand style...can be ranked as one of the best textbooks on regression in the market."
-mathermatical Reviews
"...a successful mix of theory and practice...It will serve nicely to teach both the logic behind regression and the data-analytic use of regression."
-SIAM Review

• 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

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