Page 298 - DMTH404_STATISTICS

Statistics

Notes
Figure 22.1

If all the points or dots lie exactly on a straight line or a curve, the association between the
variables is said to be perfect. This is shown below:

Figure 22.2

A scatter diagram of the data helps in having a visual idea about the nature of association
between two variables. If the points cluster along a straight line, the association between variables
is linear. Further, if the points cluster along a curve, the corresponding association is non-linear
or curvilinear. Finally, if the points neither cluster along a straight line nor along a curve, there
is absence of any association between the variables.
It is also obvious from the above figure that when low (high) values of X are associated with low
(high) value of Y, the association between them is said to be positive. Contrary to this, when low
(high) values of X are associated with high (low) values of Y, the association between them is
said to be negative.
This chapter deals only with linear association between the two variables X and Y. We shall
measure the degree of linear association by the Karl Pearson's formula for the coefficient of
linear correlation.

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