National open university of nigeria introduction to econometrics I eco 355

 
6.0 
TUTOR MARKED ASSIGNMENT
Differentiate between Modern and Financial Econometrics 
 
 
 

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43 
7.0. REFERENCES/FURTHER READINGS 
 
Medunoye, G.K., (2013). Introduction to Econometrics, 1
st
edition, Mill Publication
limited. 
Parker, J.J., (2016). Econometrics and Economic Policy, Journal vol 4, pg 43-73, Parking
& Parking Publication limited. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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44 
 
 
 
 
 
 
 
 
 
 
 
 
MODULE TWO – SINGLE- EQUATION (REGRESSION MODELS)
Unit One: 
Regression Analysis 
Unit Two: The Ordinary Least Square (OLS) Method Estimation 
Unit Three: Calculation of Parameter 
and the Assumption of Classical Least 
Regression
Method (CLRM) 
Unit Four: Properties of the Ordinary Least Square Estimators 
Unit Five: The Coefficient of Determination (R
2
): A measure of “Goodness of fit” 
 
 
 
Unit One: Regression Analysis
 
CONTENTS 
 
1.0
. Introduction 
2.0.
Objectives 
3.0.
Main content 
3.1. The Linear Regression Model 
3.2. The Classical Linear Regression Model 
3.3. Regression Vs Causation
3.4. Regression Vs Correlation
4.0 
Conclusion 
5.0 
Summary 
6.0
Tutor-Marked Assignment 
7.0 
References/Further Readings 
 
 
 

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45 
1.0 
INTRODUCTION 
The term regression was introduced by Francis Galton. In a famous paper, Galton found 
that, although there was a tendency for tall parents to have tall children and for short 
parents to have short children, the average height of children born of parents of a given 
height tended to move to ―regress‖ toward the average height in the population as a 
whole. In other words, the height of the children of unusually tall or unusually short 
parents tends to move toward the average height of the population. Galton‘s law of 
universal regression was confirmed by his friend Karl Pearson who collected more than a 
thousand records of heights of members of family groups. He found that the average 
height of sons of a group of tall fathers was less than their father‘s height and the average 
height of sons of a group of short fathers was greater than their fathers‘ height, thus 
―regressing‖‖ tall and short sons alike toward the average height of all men. In the word 
of Galton, this was ―regression to mediocrity‖. 
 
2.0. OBJECTIVES 
At the end of this unit, you should be able to: 

examine the linear regression model 

understand the classical linear regression model