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Rank Correlation - Correlation & Regression, Business Mathematics & Statistics Video Lecture | Business Mathematics and Statistics - B Com

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FAQs on Rank Correlation - Correlation & Regression, Business Mathematics & Statistics Video Lecture - Business Mathematics and Statistics - B Com

1. What is rank correlation?
Rank correlation is a statistical measure used to assess the strength and direction of the relationship between the rankings of two variables. It is also known as Spearman's rank correlation coefficient and is often used when the variables being compared are ordinal or non-normally distributed.
2. How is rank correlation different from other correlation measures?
Rank correlation measures the relationship between the rankings of variables, whereas other correlation measures such as Pearson's correlation coefficient assess the linear relationship between variables. Rank correlation is more suitable for data that do not follow a normal distribution or when the relationship is not expected to be linear.
3. How is rank correlation calculated?
Rank correlation is typically calculated using Spearman's rank correlation coefficient formula. First, the ranks of each variable are determined. Then, the differences between the ranks of the corresponding pairs of observations are squared, summed, and multiplied by 6. Finally, the rank correlation coefficient is calculated as 1 minus the division of this sum by the product of the number of observations squared minus 1.
4. What does a positive rank correlation coefficient indicate?
A positive rank correlation coefficient indicates that there is a tendency for higher ranks in one variable to correspond to higher ranks in the other variable. In other words, as the rank of one variable increases, the rank of the other variable tends to increase as well. This suggests a positive relationship or association between the variables.
5. How do you interpret the value of a rank correlation coefficient?
The value of a rank correlation coefficient can range from -1 to 1. A coefficient of -1 indicates a perfect negative rank correlation, meaning that as the rank of one variable increases, the rank of the other variable decreases perfectly. A coefficient of 1 indicates a perfect positive rank correlation, where the ranks of both variables increase perfectly together. A coefficient of 0 indicates no rank correlation or a random relationship between the variables. The closer the coefficient is to -1 or 1, the stronger the rank correlation.
115 videos|142 docs

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