Researchers are interested in examining whether female weights (lbs) can be predicted by their cholesterol levels (mg). They gathered data from the U.S. Department of Health and Human Services, National Center for Health Statistics, Third National Health and Nutrition Examination Survey and generated the following results using SAS software; however, they do not know how to interpret any of the output. Confident of your statistical abilities, they have come to you with their output and a list of questions. It is now your job to help them interpret their output. Do not let them down!

What type of analysis is this?

Select one:

a. Contingency table analysis

b. Multiple linear regression

c. Paired t-test

d. Simple linear regression

e. Two-sample t-test

What are the independent and dependent variables?

Select one:

a. Dependent variable = cholesterol, independent variable = weight

b. Dependent variable = weight, independent variable = cholesterol

c. Dependent variable is unknown, independent variable = cholesterol

d. Dependent variable unknown, independent variable = weight

e. Variable order does not matter here, i.e. (x, y) is the same as (y, x). Either cholesterol or weight can be the independent variable. The other is then the dependent variable.

What are the most appropriate null and alternative hypotheses for this analysis?

Select one:

a. H0:= 0, H1: 0

b. H0: b0= 0, H1: b0 0

c. H0: b1= 0, H1: b1 0

d. H0: 0= 0, H10 0

e. H0: 1= 0, H1: 1 0

What is the size of our sample?

Select one:

a. 38

b. 39

c. 40

d. 41

e. Cannot be determined from the given information

In the analysis of variance table, some wise guy replaced 3 important values with the letters A, B and C. You need to find these missing pieces.

Q 12Question 12

Model degrees of freedom (df), labeledA

Select one:

a. 0

b. 1

c. 2

d. 6

e. 77

Q 13Question 13

Total Sum of Squares, labeledB

Select one:

a. 33

b. 1099

c. 1415

d. 55198

e. 82797

Mean Square Error, labeledC

Select one:

a. 33

b. 1099

c. 1415

d. 55198

e. 82797

What is the value of the test statistic and thep-value that can be used to test the null hypothesis?

Select one:

a.t= 12.23,p= 0.0012

b.t= 0.29,p= 0.0012

c.t= 3.50,p= 0.0012

d.t= 14.13,p< 0.0001

e. Both a and b are correct

f. Both a and c are correct

g. Both a and d are correct

Using a 0.05 significance level, what decision and conclusion should you make regarding the null hypothesis?

Select one:

a. Becausep-value > 0.05, we fail to reject H0and conclude that cholesterol level cannot be used to predict weight.

b. Becausep-value < 0.05, we reject H0and conclude that cholesterol level can be used to predict weight.

c. Becausep-value < 0.05, we reject H0and conclude that cholesterol level can be used to predict weight.

d. Becausep-value < 0.05, we fail to reject H0and conclude that cholesterol level cannot be used to predict weight.

e. Becausep-value < 0.05, we fail to reject H0and conclude that cholesterol level can be used to predict weight.

What is the marginal change in weight, i.e. by how many lbs does weight change when cholesterol level increases by 1 mg?

Select one:

a. Increases by 0.09981 lbs

b. Decreases by 0.09981 lbs

c. Increases by 122.18 lbs

d. Decreases by 122.18 lbs

e. Increases by 24.35%

What is the estimated value of the linear correlation coefficient and how do we best interpret this value?

Select one:

a.r= 0.2435, so 24.35% of the variation in cholesterol can be explained by the linear relationship between weight and cholesterol level.

b.r= 0.4935, so 49.35% of the variation in weight can be explained by the linear relationship between weight and cholesterol level.

c.r= 0.2435, so 49.35% of the variation in cholesterol can be explained by the linear relationship between weight and cholesterol level.

d.r= 0.4935, so 24.35% of the variation in weight can be explained by the linear relationship between weight and cholesterol level.

e.r= 0.0593, so 5.93% of the variation in weight can be explained by the linear relationship between weight and cholesterol level.