1. A researcher collects a sample of 300 people and measures them on the measure

Question

1. A researcher collects a sample of 300 people and measures them on the measurement variables number of hours of TV watched per week and statistics course grade. He calculates a correlation of -.40. What proportion of the variability seen in statistics course grades can be explained or predicted by the number of hours of TV one watches per week?

.84

2. Now that the researcher from the previous question knows that there is a linear relationship between the number of hours of TV watched per week (X) and statistics course grade (Y), he decides to create a regression equation so that he can predict one’s statistics course grade (Y’) if he knows how many hours of TV one watches per week. He calculates a slope of -.78 and a y-intercept of 80 for the best-fitting line. If a student in the researcher’s class watches 13 hours of TV per week, what would the researcher predict his statistics course grade to be?

D. 90.14

3. Suppose the researcher from the previous two questions later found out that the correlation between hours of TV watched and statistics course grades was not -.40, but rather it was -.04. How does this change the predictions he can make about statistics course grades based on number of hours of TV watched?

Explanation / Answer

Answers-

1. Correlation cofficient = -0.4

So value of R2 = (-0.4)^2

= 0.16

Hence proportion of the variability seen in statistics course grades can be explained or predicted by the number of hours of TV one watches per week = 0.16

2.  slope is -.78 and y-intercept is 80, then regression equation is -

Y = 80 -0.78 * X

When X=13, then y = 69.86

Hence  his statistics course grade will be 69.86

3. When correlation cofficient is -0.04 the predictions should no longer be used because they won't be very accurate as it implies presence of very little linear relationship between the two variables and thus predictions won't be reliable.

Answers

TY!

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