The McGraw-Hill Series Economics essentials of economics brue, McConnell, and Flynn Essentials of Economics



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1.4
Regression versus Causation
Although regression analysis deals with the dependence of one variable on other variables,
it does not necessarily imply causation. In the words of Kendall and Stuart, “A statistical
relationship, however strong and however suggestive, can never establish causal connec-
tion: our ideas of causation must come from outside statistics, ultimately from some theory
or other.”
5
4
The word 
stochastic
comes from the Greek word 
stokhos
meaning “a bull’s eye.” The outcome of
throwing darts on a dart board is a stochastic process, that is, a process fraught with misses.
5
M. G. Kendall and A. Stuart, 
The Advanced Theory of Statistics,
Charles Griffin Publishers, New York,
vol. 2, 1961, chap. 26, p. 279.
guj75772_ch01.qxd 31/07/2008 11:00 AM Page 19


20
Part One
Single-Equation Regression Models
In the crop-yield example cited previously, there is no 
statistical reason
to assume that
rainfall does not depend on crop yield. The fact that we treat crop yield as dependent on
rainfall (among other things) is due to nonstatistical considerations: Common sense
suggests that the relationship cannot be reversed, for we cannot control rainfall by varying
crop yield.
In all the examples cited in Section 1.2 the point to note is that 
a statistical relationship
in itself cannot logically imply causation.
To ascribe causality, one must appeal to a priori
or theoretical considerations. Thus, in the third example cited, one can invoke economic
theory in saying that consumption expenditure depends on real income.
6
1.5
Regression versus Correlation
Closely related to but conceptually very much different from regression analysis is

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