PPT - Rank Correlation

# PPT - Rank Correlation - Business Mathematics and Statistics - B Com

``` Page 2

RANK CORRELATION
?This method is finding out the lack of it
between two variables was developed by the
british Psychologist Charles Edward
Spearman in 1904.
? In any event, the RCC is applied to a setof
ordinal rank no., with1 for the individual
ranked in quality, or quantity and so on , to n
for the individual ranked last in a group of n
individual.
Page 3

RANK CORRELATION
?This method is finding out the lack of it
between two variables was developed by the
british Psychologist Charles Edward
Spearman in 1904.
? In any event, the RCC is applied to a setof
ordinal rank no., with1 for the individual
ranked in quality, or quantity and so on , to n
for the individual ranked last in a group of n
individual.
RANK CORRELATION COEFFICIENT IS
DEFINED AS :-
R = 1 - 6?D²/N³- N
Where r denotes RCC and D
refers To the difference of the
rank between paired items in to
series.
Page 4

RANK CORRELATION
?This method is finding out the lack of it
between two variables was developed by the
british Psychologist Charles Edward
Spearman in 1904.
? In any event, the RCC is applied to a setof
ordinal rank no., with1 for the individual
ranked in quality, or quantity and so on , to n
for the individual ranked last in a group of n
individual.
RANK CORRELATION COEFFICIENT IS
DEFINED AS :-
R = 1 - 6?D²/N³- N
Where r denotes RCC and D
refers To the difference of the
rank between paired items in to
series.
Rank correlation [when rank are not
given]
?When we are given the actual data and
not the ranks, it will be necessary to
assign the ranks.
?Ranks can be assigned by taking either
the highest value as 1 or the lowest
value as 1.
Page 5

RANK CORRELATION
?This method is finding out the lack of it
between two variables was developed by the
british Psychologist Charles Edward
Spearman in 1904.
? In any event, the RCC is applied to a setof
ordinal rank no., with1 for the individual
ranked in quality, or quantity and so on , to n
for the individual ranked last in a group of n
individual.
RANK CORRELATION COEFFICIENT IS
DEFINED AS :-
R = 1 - 6?D²/N³- N
Where r denotes RCC and D
refers To the difference of the
rank between paired items in to
series.
Rank correlation [when rank are not
given]
?When we are given the actual data and
not the ranks, it will be necessary to
assign the ranks.
?Ranks can be assigned by taking either
the highest value as 1 or the lowest
value as 1.
QUESTION..
Q. Calculate spearman’s coefficient of
rank correlation for the following data:
x  : 53    98    95    81    75    61    59    55
y  : 47    25    32    37    30    40    39    45
```

115 videos|142 docs

## FAQs on PPT - Rank Correlation - Business Mathematics and Statistics - B Com

 1. What is rank correlation?
Ans. Rank correlation is a statistical measure used to determine the degree of association between two sets of rankings or ordinal data. It measures the similarity in the order of values between two variables, rather than the actual values themselves. Rank correlation is commonly used when the data is not normally distributed or when the relationship between variables is non-linear.
 2. How is rank correlation calculated?
Ans. Rank correlation is typically calculated using a correlation coefficient called Spearman's rank correlation coefficient (rho). To calculate it, the ranks of each variable are determined, and then the differences between the ranks are squared. The sum of these squared differences is then used in the formula to calculate Spearman's rho. This coefficient ranges from -1 to +1, with values close to +1 indicating a strong positive correlation, values close to -1 indicating a strong negative correlation, and values close to 0 indicating no correlation.
 3. What does a high rank correlation coefficient imply?
Ans. A high rank correlation coefficient, such as a value close to +1, implies a strong positive relationship between the two sets of rankings or ordinal data. This means that as the rank of one variable increases, the rank of the other variable also tends to increase. In other words, there is a consistent pattern of higher ranks being associated with higher ranks.
 4. Can rank correlation be used for non-parametric data?
Ans. Yes, rank correlation is particularly useful for non-parametric data, which means data that does not follow a normal distribution or have specific numerical values. Instead, non-parametric data is often ranked or categorized. Rank correlation allows us to analyze the relationship between such data without making assumptions about its distribution or specific values.
 5. How is rank correlation different from other types of correlation?
Ans. Rank correlation differs from other types of correlation, such as Pearson's correlation coefficient, in that it focuses on the order or rankings of the data rather than the actual values. Pearson's correlation coefficient measures the linear relationship between two variables using their actual values, while rank correlation measures the relationship between two sets of rankings or ordinal data. Rank correlation is also more robust to outliers and can handle non-linear relationships better than Pearson's correlation coefficient.

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