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Contents

1)     Introduction

2)   Equations – the model representation for regression

2.1 – Model Representation

2.2 – Simple Linear Regression

2.3 – Adding a Predictor to the Equation

2.4 – Other Representations – Matrix and Graphical

3)    Estimation Methods – the link between model and data

3.1 – Least Square Estimator

3.2 – Properties of OLS

3.3 – Assumptions behind OLS

3.4 – Estimation Methods Other Than OLS

4)   Explanations –meaning of coefficients

4.1 – Parameters’ Meaning

4.2 – Confidence Intervals and Tests

4.3 – Graphical Results

5)    Evaluation of Models and the Errors – fit indices

5.1 – the Residuals

5.2 – R squared

5.3 – ANOVA

5.4 – Residual plots

5.5 – Model Comparison

6)   Searching for a Best Model – a step by step modeling approach

6.1 – The Search Process

6.2 – Step 1

6.3 – Step 2

6.4 – Step 3

6.5 – Step 4

6.6 – Step 5

6.7 – Step 6

6.8 – Step 7

7)    Some Advanced Issues

7.1 – Model Specification

7.2 – Nonlinearity

7.3 – Generalization

8)   Issues Related to Using Software

8.1 – SPSS

8.2 – R and S-plus

8.3 – RM 1

9)   Conclusion