Short Questions with Answers (Part - 2) - Correlation Commerce Notes | EduRev

Statistics for Economics - Class XI

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Commerce : Short Questions with Answers (Part - 2) - Correlation Commerce Notes | EduRev

The document Short Questions with Answers (Part - 2) - Correlation Commerce Notes | EduRev is a part of the Commerce Course Statistics for Economics - Class XI.
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Q.16. What is the nature of correlation if the value of r is –1?
Ans. 
When r is –1, there is perfect negative correlation between variables.

Q.17. What is the value of r if the two variables are not related?
Ans. 
When the two variables are not related, r = 0.

Q.18. State the merits of Karl Pearson’s correlation coefficient.
Ans. 
The merits of Karl Pearson’s correlation coefficient are:
(i) It is a real measure of correlation coefficient as it is based on arithmetic mean and standard deviation.
(ii) It finds out not only the direction but also the actual degree of correlation between variables.

Q.19. What are the demerits of Karl Pearson’s correlation coefficient.
Ans. 
The demerits of Karl Pearson’s correlation coefficient are:
(i) The values of correlation coefficient are unduly affected by extreme values.
(ii) The assumption that there is a linear relation between variables is not always true.
(iii) The analysis of correlation coefficient requires a lot of care as there is a possibility of misinterpretation of results.
(iv) It involves long mathematical calculation and hence, is a time-consuming method.

Q.20. How does Spearman’s rank correlation measure relation between variables?
Ans. 
Spearman’s rank correlation measures the linear relation between ranks assigned to individual items according to their attributes.

Q.21. Name some qualitative variables.
Ans.
Examples of qualitative variables include beauty, wisdom, bravery, dedication, honesty, etc.

Q.22. Who developed Spearman’s rank correlation?
Ans. 
Spearman’s rank correlation was developed by British psychologist C.E. Spearman.

Q.23. When is rank correlation preferred to Pearsonian coefficient?
Ans. 
Rank correlation is preferred to Pearsonian coefficient when extreme values are present.

Q.24. What are the merits of rank correlation coefficient?
Ans. 
The merits of rank correlation coefficient are:
(i) It is easier to calculate and understand correlation using this method.
(ii) This method is the most appropriate for the calculation of correlation in case of qualitative and irregular facts.
(iii) This method can be employed for finding correlation even when the actual data is not given. Correlation can be found by simply using the ranks assigned to the values.

Q.25. Write the demerits of rank correlation coefficient.
Ans. 
The demerits of rank correlation coefficient are:
(i) This method is not so definite.
(ii) This method cannot be used when number of items is very high.
(iii) This method is applicable only to individual series and not for frequency distributions.

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