Question

1) A multiple regression model to predict nacho sales at a baseball game yields the following coefficients:

Intercept 1500

Home Team Score 80

Temp. (Degrees F) 100

Home Team Loss? -2000

Assuming that all variables in this model are significant, what would be the expected result of the home team scoring another run?

A- There would be no effect

B- Nacho sales would increase by 80

C- Nacho sales would decrease by 80

D- Not enough info

2) The regression models that we have attempt to describe

A- Future occurrences

B- Time series data

C- None of these

D- A linear relationship between two or more variables

3) A simple regression model is really a hypothesis tests against two models. What is the null hypothesis model?

A- The "Full Model"

B- Beta 1 (aka Intercept = 0)

C- None of these

D- The Intercept (aka Beta 1 = 0)

4) A regression model attempts to correlate a student's exam grade with the number of hours spent studying. The resulting output is:

y = 45 + 7x

If a student spent 5.5 studying hours for the exam, what is the students predicted score?

A- None of these

B- 6.5

C- 83.5

D- 52

5) True or False:

In a multiple regression model, we can only evaluate the marginal effect of a change in one variable at a time

Why is it true or false?

6) A multiple regression model returns the following overall results:

R-Square = 0.75

P-Value = 0.22

Is this a good model to use? Why or why not? What might be causing these factors?

Answer 1

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