14) Using Set up 1, once you have determined the regression equation (Y=a +bX), what is the interpretation of b?

For a 1-hour increase in studying, exam scores increase by almost 5 points

For a 1-hour increase in studying, the exam scores increase by about 1.1 points

If the student did not study at all, the expected score on the exam is about 50

If the student did not study at all, the expected score on the exam is about 52

15) Using Set up 1, what should the math professor conclude from Figure 1?

The correlation coefficient is negative.

The correlation coefficient is positive.

The correlation coefficient is strongly negative.

The correlation coefficient is strongly positive.

16) Suppose we are estimating the GPA of students using the scores on student's SAT and we find that the correlation between SAT scores and GPA is close to +1. For those students who scored one standard deviations above the mean SAT score, using the regression method, what is the guess for their average GPA?

About 1 standard deviation above the average GPA

About 1 standard deviation below the average GPA

About 2 standard deviations above the average GPA

About 1.5 standard deviations above the average GPA

17) concerned that freshman may suffer from more bouts of depression than other students. To test this, the university gives a random set of 100 students a test for depression which creates a scale from 1 to 100 with higher numbers indicating more difficulty with depression. Since other factors affect mental health, such as workload, income level, etc., the study controls for those other factors. How might the study address the issue of a potential difference between freshman and other students?

Use a categorical dummy variable coded 1 for freshman and 0 for other.

Use a categorical dummy variable coded 1 for freshman and 2 for sophomore and ignore juniors and seniors.

Drop all freshman from the sample

There is no way to test this theory.

18)

A sales manager for an advertising agency believes there is a relationship between the number of contacts (with prospective clients) and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t Stat P-value Intercept -9.058 5.88 -1.549 0.158 Numbrer of Contacts 3.169 0.196 16. 168 0.000 What is the regression equation? O Y = 3.169 - 9.058X O Y = -12.201 + 2.195X O Y = 12.201 + 2.195X O Y = -9.058 + 3.169XWhat is the R-Squared value from the below data? Table 2 ANOVA of SS MS F Significance F Regression 1 2,356.9 2,356.9 110.3 3.2E-11 Residual 28 598.2 21.4 Total 29 2,955.1 O 0.5358 0 0.7320 O 0.7976 O 0.8931Question 20 Using Table 2, what is the value of the correlation coefficient? O 0.5358 O 0.7320 0 0.7976 O 0.8931When calculating the standard error of the estimate in linear regression using a sample with n observations, we first calculate the Sum of Squares Error (SSE) as and then divide by and then O n ( D 1 - 7 ) 2 i=1 ; n-2; take the square root of the result O n i=1 ; n-2; take the square root of the result O n (yi - y)2; n-1; multiply by the square root of the result i=1 O n (v1 - >;)2; n-2; use the result as the standard error of the estimate 1=1