National open university of nigeria introduction to econometrics I eco 355

 
 
4.0 
CONCLUSION 
In statistics, ordinary least squares (OLS) or linear least squares is a method for 
estimating the unknown parameters in a linear regression model, with the goal of 
minimizing the differences between the observed responses in some arbitrary dataset and 
the responses predicted by the linear approximation of the data (visually this is seen as 
the sum of the vertical distances between each data point in the set and the corresponding 
point on the regression line - the smaller the differences, the better the model fits the 
data). The resulting estimator can be expressed by a simple formula, especially in the 
case of a single regressor on the right-hand side. 
 
5.0 SUMMARY 
 
The unit makes us to understand how to derive the ordinary least square (OLS) method of 
estimation of Residual sum of square 
̂
and to know the technique to be used in deriving 
them. 
 
6.0 
TUTOR MARKED ASSIGNMENT EXERCISES 
1.
Derive the Residual Sum of Square parameter
̂
. 
 
 
7.0 
REFERENCES/Further Reading 

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52 
Dimitrios, A & Stephen, G., (2011). Applied Econometrics, second edition 2011, first
edition 2006 and revised edition 2007. 
UNIT 3 
CALCULATION OF PARAMETER 
̂
 AND THE ASSUMPTION OF
CLRM 
 
CONTENTS 
 
1.0.
Introduction 
2.0.
Objectives 
3.0.
Main content 
3.1. Alternative Expression for 
3.2. The Assumptions of CLRM 
3.2.1. The Assumptions
4.0 
Conclusion 
5.0 
Summary 
6.0
Tutor-Marked Assignment 
7.0 
References/Further Readings