National open university of nigeria introduction to econometrics I eco 355

6.0 
TUTOR MARKED ASSESSMENT EXERCISE 
1. 
Discuss the differences between Efficiency and BLUEness. 
2. 
Proof the consistency property of classical linear regression model. 
7.0 
REFERENCES 
 
 
Dimitrios, A & Stephen, G., (2011). Applied Econometrics, second edition 2011, first
edition 2006 and revised edition 2007. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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63 
 
UNIT 5 : THE COEFFICIENT OF DETERMINATION
: A MEASURE OF
“GOODNESS OF FIT”. 
 
CONTENTS 
 
1.0.
Introduction 
2.0.
Objectives 
3.0.
Main content 
3.1. Goodness of fit 
4.0 
Conclusion 
5.0 
Summary 
6.0
Tutor-Marked Assignment 
7.0. 
References/Further Readings 
1.0. INTRODUCTION 
In statistics, the coefficient of determination denoted 
R
2
or 
r
2
and pronounced R squared, 
is a number that indicates the proportion of the variance in the dependent variable that is 
predictable from the independent variable. 
It is a statistic used in the context of statistical models whose main purpose is either the 
prediction of future outcomes or the testing of hypotheses, on the basis of other related 
information. It provides a measure of how well observed outcomes are replicated by the 
model, based on the proportion of total variation of outcomes explained by the model 
(pp. 187, 287). 
There are several definitions of 
R
2
that are only sometimes equivalent. One class of such 
cases includes that of simple linear regression where 
r
2
is used instead of 
R
2
. In this case, 
if an intercept is included, then 
r
2
is simply the square of the sample correlation 
coefficient (i.e., 
r
) between the outcomes and their predicted values. If additional 
explanator are included, 
R
2
is the square of the coefficient of multiple correlations. In 
both such cases, the coefficient of determination ranges from 0 to 1. 
Important cases where the computational definition of 
R
2
can yield negative values, 
depending on the definition used, arise where the predictions that are being compared to 
the corresponding outcomes have not been derived from a model-fitting procedure using 
those data, and where linear regression is conducted without including an intercept. 

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64 
Additionally, negative values of 
R
2
may occur when fitting non-linear functions to data. 
In cases where negative values arise, the mean of the data provides a better fit to the 
outcomes than do the fitted function values, according to this particular criterion.