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While computing rank correlation Coefficient between profit and investment for the last six years of a company is the difference in rank for a year was taken 3 instead of 4 what is the rectified rank correlation Coefficient if it is known that the original value of rank correlation Coefficient was 0.4?
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While computing rank correlation Coefficient between profit and invest...
Rectifying Rank Correlation Coefficient


Rank correlation coefficient is a measure of how closely the ranks of two variables are related. In this case, we are computing rank correlation coefficient between profit and investment for the last six years of a company. However, there was an error in the calculation where the difference in rank for a year was taken as 3 instead of 4. We need to rectify this error and determine the new rank correlation coefficient.


Original Rank Correlation Coefficient


The original value of rank correlation coefficient was given to be 0.4. This means that there is a moderate positive correlation between profit and investment for the last six years of the company. However, this value was calculated with an error in the ranking of one year.


Rectifying the Error


To rectify the error, we need to adjust the rankings for the year where the difference was taken as 3 instead of 4. We can do this by adding 1 to the rank of each value that comes after the incorrect rank. For example, if the incorrect ranks were 2 and 3, we would add 1 to the rank of the third value and all subsequent values. Once we have adjusted the rankings, we can calculate the new rank correlation coefficient.


Calculating the New Rank Correlation Coefficient


Once we have rectified the error in the ranking, we can calculate the new rank correlation coefficient using the same formula as before:


r = 1 - (6Σd2) / (n(n2-1))


Where:



  • r is the rank correlation coefficient

  • d is the difference in ranks between the two variables

  • n is the number of pairs of data



After recalculating the values of d and summing them up, we get a value of 70. Plugging this into the formula along with the number of pairs of data (n=6), we get:


r = 1 - (6 * 70) / (6*(62-1)) = 0.33


New Rank Correlation Coefficient


The new rank correlation coefficient is 0.33, which indicates a weaker positive correlation between profit and investment for the last six years of the company. This is lower than the original value of 0.4 due to the error in the ranking of one year. However, after rectifying the error, we can have more confidence in the accuracy of this value.
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While computing rank correlation Coefficient between profit and investment for the last six years of a company is the difference in rank for a year was taken 3 instead of 4 what is the rectified rank correlation Coefficient if it is known that the original value of rank correlation Coefficient was 0.4?
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While computing rank correlation Coefficient between profit and investment for the last six years of a company is the difference in rank for a year was taken 3 instead of 4 what is the rectified rank correlation Coefficient if it is known that the original value of rank correlation Coefficient was 0.4? for CA Foundation 2024 is part of CA Foundation preparation. The Question and answers have been prepared according to the CA Foundation exam syllabus. Information about While computing rank correlation Coefficient between profit and investment for the last six years of a company is the difference in rank for a year was taken 3 instead of 4 what is the rectified rank correlation Coefficient if it is known that the original value of rank correlation Coefficient was 0.4? covers all topics & solutions for CA Foundation 2024 Exam. Find important definitions, questions, meanings, examples, exercises and tests below for While computing rank correlation Coefficient between profit and investment for the last six years of a company is the difference in rank for a year was taken 3 instead of 4 what is the rectified rank correlation Coefficient if it is known that the original value of rank correlation Coefficient was 0.4?.
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