A regression between the returns on a stock and its industry index returns gives the following results: Coefficient- Standard Error- t-value Intercept 2.1, 2.01, 1.04 Industry Index 1.9, 0.31, 6.13 The t-statistic critical value at the 0.01 level of significance is 2.58 Standard error of estimate = 15.1 Correlation coefficient = 0.849 The regression statistics presented indicate that the dispersion of stock returns about the regression line is: A) 63.20. B) 72.10. C) 7.75. D) 15.10.
D?
such a weird way to ask question “the dispersion of return about the regression line”? D.
I would go with D also, but I feel like I am being trapped.
The standard error of estimate is the standard error of the error term. If you have a big dispersion of returns about the regression line then you’ll have a big standard error in your error term. So the dispersion can be measured by the standard error of estimate. The wider the dispersion, the larger the standard error of estimate. So my answer would be D. But I agree that it’s a wierd way to ask the question.
We all are good with D. There is no trap here.
I felt trapped too… such a simple concept and they make it sound like it’s astro physics.
same here, I almost got trapped by all the details given. Its D
D … without a doubt