Question

when to use the method of actual data and concurrent deviation method of correlation​

Answer 1

A very simple and casual method of finding correlation when we are not serious about the magnitude of the two variables is the application of concurrent deviations.

This method involves in attaching a positive sign for a x-value (except the first) if this value is more than the previous value and assigning a negative value if this value is less than the previous value.

This is done for the y-series as well. The deviation in the x-value and the corresponding y-value is known to be concurrent if both the deviations have the same sign.

Denoting the number of concurrent deviation by c and total number of deviations as m (which must be one less than the number of pairs of x and y values), the coefficient of concurrent-deviations is given by

Concurrent Deviation - Correlation & Regression, Business Mathematics & Statistics

If (2c–m) > 0, then we take the positive sign both inside and outside the radical sign and if (2c–m) < 0, we are to consider the negative sign both inside and outside the radical sign.

Like Pearson’s correlation coefficient and Spearman’s rank correlation coefficient, the coefficient of concurrent- deviations also lies between –1 and 1, both inclusive.

That is,

-1 ≤ r ≤ 1

 

Coefficient of concurrent-deviations - Practice problem

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cRAZY pRINCE

Answer 2

When the changes take place in same direction in two variables or data series, the correlation between them is said to be positive and direct. For example, if increase in one variable causes increase in the other variable or a decrease in one variable causes decrease in the other variable, the two variables show positive correlation.

But when the changes in two variables occur in opposite directions, the correlation is said to be inverse or indirect or negative, i.e., if increase in one variable may cause decrease in the other or vice-versa, the two variables show negative or inverse correlation.