81. If bxy = 0.25 and byx = 0.64 correlation coefficient is
82. Assertion (A): A reasonably large sized sample drawn randomly from a given population contains almost all the characteristics of the population.
Reason (R): As per the sampling theory, the assertion is based only on the 'law of inertia of large numbers'.
Reason (R): As per the sampling theory, the assertion is based only on the 'law of inertia of large numbers'.
83. Which one of the following is not a measure of dispersion?
84. Assertion (A): When there is an evidence of a linear relationship between two variables, it may not always mean an independent-dependent relationship between the two variables.
Reason (R): The casual relationship between the two variables may not imply a reasonable theoretical relationship between the two.
Reason (R): The casual relationship between the two variables may not imply a reasonable theoretical relationship between the two.
85. If there are 8 possible classes under consideration for a goodness of fit, the number of degrees of freedom will be
86. If the correlation coefficientis a positive value, then the slope of the regression line:
87. In an exclusive class
88. The probable error of the coefficient of correlation(r) is calculated by which one of the following formula?
89. If two events A and B are dependent, the conditional probability of A given B, i.e., P (A/B) is calculated as:
90. Match List-I with List-II and select the correct answer:
List-I
List-II
a. Coefficient of determination
1. $${\gamma _{xy}}\frac{{{\sigma _x}}}{{{\sigma _y}}}$$
b. Spearman's rank correlation coefficient
2. $$1 - \frac{{6\sum {{d^2}} }}{{n\left( {{n^2} - 1} \right)}}$$
c. Regression coefficient of $$x$$ on $$y$$ variable
3. $$\frac{{\sum {xy} }}{{n\,{\sigma _x}\,{\sigma _y}}}$$
d. Karl Pearson's formula of calculating $$\gamma $$
4. $${\gamma ^2}$$
| List-I | List-II |
| a. Coefficient of determination | 1. $${\gamma _{xy}}\frac{{{\sigma _x}}}{{{\sigma _y}}}$$ |
| b. Spearman's rank correlation coefficient | 2. $$1 - \frac{{6\sum {{d^2}} }}{{n\left( {{n^2} - 1} \right)}}$$ |
| c. Regression coefficient of $$x$$ on $$y$$ variable | 3. $$\frac{{\sum {xy} }}{{n\,{\sigma _x}\,{\sigma _y}}}$$ |
| d. Karl Pearson's formula of calculating $$\gamma $$ | 4. $${\gamma ^2}$$ |
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