merits and demerits of karl Pearson's coefficient of correlation
Answers
This technique not just shows the presence, or nonappearance of the relationship between's any two factors yet, in addition, decides the correct degree, or how much they have corresponded.
Under this strategy, we can likewise discover the bearing of the connection i.e. regardless of whether the connection between's the two factors is positive, or negative.
This technique empowers us in evaluating the estimation of a reliant variable with reference to a specific estimation of an autonomous variable through relapse conditions.
Demerits:
It is nearly hard to calculate as its calculation includes complicated logarithmic strategies for figurings.
It is particularly influenced by the estimations of the outrageous things.
It depends on a substantial number of presumptions viz. straight relationship, circumstances, and end results in a relationship and so on which may not generally hold good.
Merits
The following are the chief points of merit that go in favour of the Karl Pearson’s method of correlation:
This method not only indicates the presence, or absence of correlation between any two variables but also, determines the exact extent, or degree to which they are correlated.
Under this method, we can also ascertain the direction of the correlation i.e. whether the correlation between the two variables is positive, or negative.
This method enables us in estimating the value of a dependent variable with reference to a particular value of an independent variable through regression equations.
This method has a lot of algebraic properties for which the calculation of co-efficient of correlation, and a host of other related factors viz. co-efficient of determination, are made easy.
Demerits
Despite the above points of merits, this method also suffers from the following demerits:
It is comparatively difficult to calculate as its computation involves intricate algebraic methods of calculations.
It is very much affected by the values of the extreme items.
It is based on a large number of assumptions viz. linear relationship, cause and effect relationship etc. which may not always hold good.
It is very much likely to be misinterpreted particularly in case of homogeneous data.
In comparison to the other methods, it takes much time to arrive at the results.
It is subject to probable error which its propounder himself admits, and therefore, it is always advisable to compute it probable error while interpreting its results.