4.1 Correlation — Test 1 — RBR Life Science

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4.1 Correlation — Test 1

Q1. Correlation measures the:✓ Degree and direction of the relationship between two variables

Q2. The coefficient of correlation (r) can take any value between:✓ −1 and +1

Q3. The most widely used measure of linear correlation is:✓ Karl Pearson's coefficient of correlation

Q4. When two variables move in the same direction, the correlation is:✓ Positive

Q5. When one variable increases as the other decreases, the correlation is:✓ Negative

Q6. A correlation coefficient of 0 indicates:✓ No linear correlation between the variables

Q7. The graphical method of studying correlation uses a:✓ Scatter diagram (scattergram)

Q8. Spearman's rank correlation is appropriate when the data are:✓ Ranked (ordinal) or not normally distributed

Q9. A correlation coefficient of +1 represents:✓ A perfect positive correlation

Q10. An important caution about correlation is that correlation does not imply:✓ Causation

Q11. The correlation coefficient is a:✓ Unit-free (dimensionless) number

Q12. The coefficient of determination is:✓ The square of the correlation coefficient (r²)

Q13. A correlation coefficient close to 0 (e.g. 0.05) indicates a relationship that is:✓ Very weak (little linear association)

Q14. Pearson's correlation coefficient specifically measures the strength of a:✓ Linear relationship

Q15. The correlation coefficient between X and Y is:✓ The same as the correlation between Y and X (symmetric)

Q16. In a scatter diagram, points that cluster tightly around an upward straight line indicate:✓ A strong positive correlation

Q17. If r² = 0.49, the correlation coefficient r could be:✓ +0.7 or −0.7

Q18. A strong correlation between two variables means that one variable can be used to:✓ Reasonably predict the other

Q19. The value of the correlation coefficient is unaffected by:✓ A change of origin and scale of the variables

Q20. Match each correlation value/term with its meaning and select the correct option.✓ A-iii, B-iv, C-i, D-ii