Applied Regression Analysis
eBook - PDF

Applied Regression Analysis

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Applied Regression Analysis

About this book

An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.

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Yes, you can access Applied Regression Analysis by Norman R. Draper,Harry Smith in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley-Interscience
Year
2014
Print ISBN
9780471170822
eBook ISBN
9781118625620
Edition
3
Topic
Mathematics
Subtopic
Probability & Statistics
Index
Mathematics

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Contents
  5. Preface to the Third Edition
  6. About the Software
  7. Chapter 0: Basic Prerequisite Knowledge
  8. Chapter 1: Fitting a Straight Line by Least Squares
  9. Chapter 2: Checking the Straight Line Fit
  10. Chapter 3: Fitting Straight Lines: Special Topics
  11. Chapter 4: Regression in Matrix Terms: Straight Line Case
  12. Chapter 5: the General Regression Situation
  13. Chapter 6: Extra Sums of Squares and Tests for Several Parameters Being Zero
  14. Chapter 7: Serial Correlation in the Residuals and the Durbin–watson Test
  15. Chapter 8: More on Checking Fitted Models
  16. Chapter 9: Multiple Regression: Special Topics
  17. Chapter 10: Bias in Regression Estimates, and Expected Values of Mean Squares and Sums of Squares
  18. Chapter 11: on Worthwhile Regressions, Big F’s, and R2
  19. Chapter 12: Models Containing Functions of the Predictors, Including Polynomial Models
  20. Chapter 13: Transformation of the Response Variable
  21. Chapter 14: “dummy” Variables
  22. Chapter 15: Selecting the “best” Regression Equation
  23. Chapter 16: Ill-conditioning in Regression Data
  24. Chapter 17: Ridge Regression
  25. Chapter 18: Generalized Linear Models (glim)
  26. Chapter 19: Mixture Ingredients as Predictor Variables
  27. Chapter 20 the Geometry of Least Squares
  28. Chapter 21: More Geometry of Least Squares
  29. Chapter 22: Orthogonal Polynomialsand Summary Data
  30. Chapter 23: Multiple Regression Applied to Analysis of Variance Problems
  31. Chapter 24: an Introduction to Nonlinear Estimation
  32. Chapter 25: Robust Regression
  33. Chapter 26: Resampling Procedures (bootstrapping)
  34. Bibliography
  35. True/false Questions
  36. Answers to Exercises
  37. Tables
  38. Index of Authors Associated with Exercises
  39. Index