Lesson 4B.3.1-4B.3.2
4B.3.1-4B.3.2 Spearman's rank correlation coefficient Quiz: Pearson Edexcel Further Maths, Unit 20
20 questions
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Lesson 4B.3.1-4B.3.2, Spearman's rank correlation coefficient: 20 multiple choice questions for the Pearson Edexcel Further Maths (9FM0), Unit 20: Non-parametric tests, written with Revision Ninja.
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The 20 questions
-
Which formula gives Spearman's rank correlation coefficient r_s for n pairs of ranks with differences d?
- r_s = 6 sum(d^2) / (n(n^2 - 1)) - 1
- r_s = 1 - sum(d^2) / n^2
- r_s = 1 - 6 sum(d^2) / (n(n^2 - 1))
- r_s = 1 - 6 sum(d^2) / (n(n + 1))
-
What does r_s = +1 indicate about two sets of ranked data?
- The rankings agree perfectly, so the higher-ranked item on one variable is always higher-ranked on the other
- There is no association between the two variables
- The two variables are statistically independent of each other
- The two sets of raw data have identical means
-
What does r_s = -1 indicate about two sets of ranked data?
- The two sets of ranks are identical
- The rankings are in exactly reversed order
- There is no linear relationship between the variables
- The two variables have equal variances
-
What does Spearman's rank correlation coefficient use in its calculation?
- The deviations of each value from the mean
- The raw values after standardising them with z-scores
- The ranks of the data values rather than the raw values
- Only the largest value in each sample
-
How should tied values be ranked when calculating Spearman's coefficient?
- Each tied value is given the lowest rank among them
- Each tied value is given the highest rank among them
- Each tied value is given the mean of the ranks they would otherwise occupy
- Tied values are removed from the data before ranking
-
When is Spearman's rank correlation a suitable measure of association?
- Only when both variables are normally distributed before any calculation
- When the data are ordinal, or the relationship is monotonic but not linear, and normality is not assumed
- Only when the relationship is linear and the sample is very large
- Only when both variables use equally spaced interval data
-
In the formula for r_s, what does d represent?
- The difference between the two ranks of each pair
- The product of the two ranks of each pair
- The difference between the two raw values of each pair
- The sum of the two ranks of each pair
-
Six pairs of ranks have sum(d^2) = 10. What is r_s?
- 0.857
- 0.5
- 5/7, about 0.714
- 2/7, about 0.286
-
Eight pairs of ranks have sum(d^2) = 42. What is r_s?
- -0.5
- 0.75
- 0.25
- 0.5
-
Five pairs of ranks have sum(d^2) = 4. What is r_s?
- 0.8
- 0.6
- -0.8
- 0.2
-
Ranks of x are 1, 2, 3, 4, 5 and ranks of y are 5, 4, 3, 2, 1. What is r_s?
- -0.5
- 0
- 1
- -1
-
Values of x are 2, 4, 4, 7, 9. What rank is given to each of the two values equal to 4?
- 2
- 2.5
- 2.25
- 3
-
Seven students are ranked on maths and physics, and r_s = -0.6. Which interpretation is correct?
- Students tend to be ranked in opposite order on the two subjects, showing a moderately strong negative association
- The mean maths and physics scores are equal
- Students with higher maths ranks tend to have higher physics ranks
- Maths and physics performance are unrelated
-
Four pairs of ranks: x ranks 1, 2, 3, 4 and y ranks 2, 1, 4, 3. What is r_s?
- 0.8
- 0.6
- 0.2
- 0.4
-
Twelve pairs of ranks have sum(d^2) = 100. What is r_s, to 3 decimal places?
- 0.650
- -0.650
- 0.350
- 0.500
-
A researcher finds r_s = 0.9 between two variables. Which statement is correct?
- The result shows that the regression slope is exactly 1
- The result shows a strong monotonic association but does not establish causation
- The result proves that one variable causes the other
- The result shows that both variables are normally distributed
-
Two variables satisfy Y = X^3 for all positive X. What is the Spearman rank correlation between their data?
- about 0.5
- -1
- 0
- 1
-
Five pairs of ranks give sum(d^2) = 34. What is r_s?
- 0.7
- 0.3
- -0.7
- -1.7
-
Four pairs of ranks have r_s = 0.4. What is the value of sum(d^2)?
- 6
- 3
- 12
- 24
-
Why is Spearman's rank correlation less affected by outliers than the product moment correlation?
- Ranking compresses extreme values, so an outlier's size does not affect its rank beyond its position
- Ranking doubles the sample size, which dilutes the outlier
- Ranking removes the outlier from the data before the calculation starts
- Ranking forces all values to have the same variance
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