1. The frequency distribution of wages is given below:
The percentage of workers who earned between Rs 80 and Rs 125 is:
(a) 60%
(b) 50%
(c) 40%
(d) 30%
Answer: (b)
2. The following data are given on length (X) in cm and weight (Y) in gm of 6 tubes:
Which one of the following is correct in respect of above data?
(a) X is more varying than Y
(b) Y is more varying than X
(c) X and Y have the same variance
(d) X and Y have the same mean
(a) X is more varying than Y
(b) Y is more varying than X
(c) X and Y have the same variance
(d) X and Y have the same mean
Answer: (a)
3. Let X , Y and Z denote the scores of golfers A, B and C respectively, in a match. The following data were obtained from 50 matches:
The golfer who is most consistent is:
(a) A
(b) B
(c) C
(d) Cannot be determined
(a) A
(b) B
(c) C
(d) Cannot be determined
Answer: (a)
4.
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)
(a) Gauss’s correction
(b) Sheppard’s correction
(c) Yates’ correction
(d) Bessel’s correction
(b) Sheppard’s correction
(c) Yates’ correction
(d) Bessel’s correction
Answer: (c)
5. Consider the following statements:
1. Karl Pearson’s correlation coefficient measures the degree of linear relationship between
two variables
2. Karl Pearson’s correlation coefficient lies between ‐1 and 1 including limits
3. Karl Pearson’s correlation coefficient is not independent of change of origin and scale
1. Karl Pearson’s correlation coefficient measures the degree of linear relationship between
two variables
2. Karl Pearson’s correlation coefficient lies between ‐1 and 1 including limits
3. Karl Pearson’s correlation coefficient is not independent of change of origin and scale
Which of the above statements is/ are correct?
(a) 1 and 2 only
(b) 1 only
(c) 2 and 3 only
(d) 1, 2 and 3
(a) 1 and 2 only
(b) 1 only
(c) 2 and 3 only
(d) 1, 2 and 3
Answer: (a)
6. Consider the following statements:
1. The values of regression coefficients of Y on X and X on Y are 0.96 and 0.79 respectively.
2. Regression coefficients are independent of change of origin but not of scale.
3. Correlation coefficient is the geometric mean between regression coefficients.
1. The values of regression coefficients of Y on X and X on Y are 0.96 and 0.79 respectively.
2. Regression coefficients are independent of change of origin but not of scale.
3. Correlation coefficient is the geometric mean between regression coefficients.
Which of the above statements are correct?
(a) 2 and 3 only
(b) 1 and 3 only
(c) 1 and 2 only
(d) 1, 2 and 3
(a) 2 and 3 only
(b) 1 and 3 only
(c) 1 and 2 only
(d) 1, 2 and 3
Answer: (d)
7. Consider the following statements:
1. Geometric mean is used to study the relative changes of price level in two time periods.
2. Geometric mean gives more weightage to small items compared to Harmonic mean.
1. Geometric mean is used to study the relative changes of price level in two time periods.
2. Geometric mean gives more weightage to small items compared to Harmonic mean.
Which of the above statements is/are correct?
(a) 1 only
(b) 2 only
(c) Both 1 and 2
(d) Neither 1 nor 2
(a) 1 only
(b) 2 only
(c) Both 1 and 2
(d) Neither 1 nor 2
Answer: (a)
8. Consider the following statements:
1. Quantitative data can be measured in ratio scale
2. Categorical data can be measured in either ordinal or interval scale.
1. Quantitative data can be measured in ratio scale
2. Categorical data can be measured in either ordinal or interval scale.
Which of the above statements is/are correct?
(a) 1 only
(b) 2 only
(c) Both 1 and 2
(d) Neither 1 nor 2
(a) 1 only
(b) 2 only
(c) Both 1 and 2
(d) Neither 1 nor 2
Answer: (a)
9. Consider the following data about the classes A, B and C:
![](data:image/png;base64,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)
If five students are transferred from A to B, then what will happen to the average score of B?
(a) It will remain constant
(b) It will definitely decrease
(c) It will definitely increase
(d) Cannot say
(a) It will remain constant
(b) It will definitely decrease
(c) It will definitely increase
(d) Cannot say
Answer: (b)
10. Consider the following statements:
1. Mean is not amenable to algebraic treatment.
2. Mode is not affected by extreme observations.
3. Median is not amenable to algebraic treatment.
1. Mean is not amenable to algebraic treatment.
2. Mode is not affected by extreme observations.
3. Median is not amenable to algebraic treatment.
Which of the above statements are correct?
(a) 1 and 2 only
(b) 1 and 3 only
(c) 2 and 3 only
(d) 1, 2 and 3
(a) 1 and 2 only
(b) 1 and 3 only
(c) 2 and 3 only
(d) 1, 2 and 3
Answer: (c)
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