Introduction to regression analysis / M.A. Golberg & H.A. Cho.

CONTENT


1. INTRODUCTION
1.1 A brief introduction to regression
1.2 Typical application of regression analysis
1.3 Computer usage

2. SOME BASIC RESULTS IN PROBALILITIES AND STATICTICS
2.1 Introduction
2.2 Probability spaces
2.3 Random variables
2.4 The probability distribution of x
2.5 Some random variables and their distribution
etc

3. Simple linear regression
3.1 Introduction
3.2 The error model
3.3 Estimating
3.4 Properties
3.5 The gause markov theorem
etc

4 .Random vectors and matrix algerbra
4.1 Introduction
4.2 Matrices and vectors
4.3 Fundamentals of matrix algebra
4.4 Matrices and linear transformation
4.5 The geometry of vectors
etc

5. Multiple regression
5.1 Introduction
5.2 The general linear model
5.3 Least square estimation
etc

6. Resiidual, Dignostics and transformation
6.1 Introduction
6.2 Residuals
6.3 Residual plots
6.4 PRESS residual
6.5 Transformation
etc

7. Further applications of regression techniques
7.1 Introduction
7.2 Polynomial models on one variable
7.3 Radial basic functions
7.4 Dummy basic functions
7.5 Interactions

8. Selection of regression model
8.1 Introduction
8.2 Consequences of model misspecification
8.3 Criteria functions
8.4 Various methods of model selection
8.5 Exercises

9. Multicollinearity : Diagnosis and remedies
9.1 Introduction
9.2 Detecting multicollinearity
9.3 Other multicollinearity diagnosis
9.4 Combatting multicollinearity
9.5 Biased estimation
etc

Bibliography : p 421-428 ._ Index : p429-436