1. Correlation analysis helps to measure:
A. Cause and effect relationship
B. Degree of association between two variables
C. Independence of variables
D. Variation of one variable only
Answer: B
Rationale: Correlation measures the strength and direction of association between two variables, not causation.
2. The most common measure of correlation is:
A. Karl Pearson’s coefficient
B. Chi-square test
C. Regression coefficient
D. t-test
Answer: A
Rationale: Pearson’s correlation coefficient (r) is the most widely used to measure linear relationships.
3. The range of correlation coefficient (r) is:
A. 0 to 1
B. –1 to +1
C. –∞ to +∞
D. 0 to 100
Answer: B
Rationale: The correlation coefficient always lies between –1 and +1.
4. A correlation coefficient of zero indicates:
A. Strong positive relationship
B. Strong negative relationship
C. No linear relationship
D. Perfect relationship
Answer: C
Rationale: When r = 0, there’s no linear correlation, though non-linear relations may exist.
5. Perfect positive correlation means:
A. r = 0
B. r = 1
C. r = –1
D. r = 0.5
Answer: B
Rationale: When r = +1, the variables move exactly in the same direction.
6. In agriculture, correlation is used mainly to:
A. Predict prices only
B. Study relationships among yield and yield components
C. Detect soil texture
D. Study weather only
Answer: B
Rationale: Correlation helps determine how traits (like plant height, grain yield, etc.) are related.
7. If r = –0.9, the relationship is:
A. Weak positive
B. Strong negative
C. No correlation
D. Moderate positive
Answer: B
Rationale: A value close to –1 indicates a strong negative association.
8. The square of the correlation coefficient (r²) is known as:
A. Regression coefficient
B. Coefficient of determination
C. Partial correlation
D. Coefficient of variation
Answer: B
Rationale: r² indicates how much variation in one variable is explained by another.
9. In correlation analysis, if the data is not normally distributed, one should use:
A. Karl Pearson’s r
B. Spearman’s rank correlation
C. Regression
D. Mean deviation
Answer: B
Rationale: Spearman’s rank correlation is non-parametric and suitable for ordinal or non-normal data.
10. Positive correlation between grain yield and number of tillers implies:
A. Increase in one leads to decrease in other
B. Both increase together
C. Both decrease together
D. No relation
Answer: B
Rationale: Positive correlation means an increase in tiller number is associated with an increase in yield.
11. Which of the following correlation coefficients shows the weakest relationship?
A. 0.90
B. –0.45
C. 0.10
D. –0.80
Answer: C
Rationale: The closer r is to 0, the weaker the relationship.
12. When one variable increases while the other decreases, the correlation is:
A. Positive
B. Negative
C. Zero
D. Constant
Answer: B
Rationale: Negative correlation means inverse relationship between variables.
13. Correlation analysis cannot establish:
A. Relationship between variables
B. Strength of relationship
C. Cause-effect relationship
D. Direction of relationship
Answer: C
Rationale: Correlation does not imply causation; it only shows association.
14. A high correlation between yield and rainfall suggests:
A. Rainfall causes high yield
B. There is an association between rainfall and yield
C. No relationship exists
D. Rainfall does not affect yield
Answer: B
Rationale: Correlation shows association, not necessarily causation.
16. Correlation analysis is applicable when:
A. Both variables are qualitative
B. Both variables are quantitative
C. One variable is categorical
D. None of the above
Answer: B
Rationale: Correlation applies to numerical variables.
17. Which test is used to determine the significance of a correlation coefficient?
A. F-test
B. Chi-square test
C. t-test
D. Z-test
Answer: C
Rationale: t-test is used to check if the correlation coefficient differs significantly from zero.
18. In crop improvement, correlation between traits helps breeders to:
A. Estimate direct and indirect effects
B. Measure genetic drift
C. Measure soil fertility
D. Predict photosynthetic rate
Answer: A
Rationale: Correlation analysis assists in identifying traits that influence yield directly or indirectly.
19. If two traits have high correlation but low heritability, it implies:
A. Environmental influence is high
B. Traits are purely genetic
C. Correlation is meaningless
D. Traits are independent
Answer: A
Rationale: High correlation with low heritability suggests strong environmental effects.
20. In agricultural experiments, correlation analysis is often followed by:
A. Analysis of variance
B. Path coefficient analysis
C. Randomization
D. Factorial design
Answer: B
Rationale: Path analysis decomposes correlation into direct and indirect effects.
21. A correlation of –1 means:
A. Variables move together
B. Variables move oppositely in a perfect linear fashion
C. No relationship
D. Non-linear relationship
Answer: B
Rationale: Perfect negative correlation means exact inverse movement.
22. If rainfall and yield are positively correlated, a drought year would likely result in:
A. Higher yield
B. Lower yield
C. No change
D. Uncertain effect
Answer: B
Rationale: Positive correlation means both move in the same direction.
23. The correlation coefficient is dimensionless because:
A. It has no units
B. It is a ratio of standardized variables
C. It depends on the mean
D. It depends on sample size
Answer: B
Rationale: It’s calculated using standardized deviations of variables.
24. The presence of outliers in the data may:
A. Have no effect on r
B. Inflate or deflate the correlation coefficient
C. Make correlation more accurate
D. Increase sample size
Answer: B
Rationale: Outliers can distort correlation strength and direction.
25. A correlation matrix is useful for:
A. Studying multiple pairwise relationships simultaneously
B. Testing regression slopes
C. Computing means
D. Designing experiments
Answer: A
Rationale: A correlation matrix shows correlations among several variables in one table.
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