Bradys Beer Store asked you to develop a way to forecast

Brady’s Beer Store asked you to develop a way to forecast beer sales. You determined two factors that influence beer sales for Brady’s Beer Store: the temperature outside and the number of people (age 21 or over) passing in front of the store. You collected data over a period of time. You then analyzed a random sample of 20 days using multiple regression.

Y = the number of six-packs of beer sold each day
X1 = the daily high temperature
X2 = the daily traffic count
The partial computer output is below. Use it to answer the questions below.

1

Analyze the correlation matrix.

1. What is the correlation between beer sales and the daily high temperature? 


2. Isthispositive? 


3. What is the correlation between beer sales and the daily traffic count? 


4. Is this positive? 


T-Values

5. What is the calculated T Value for the variable X1? (This was actually discussed on page 237 in Chapter 6) 


6. What is the critical T Value at the .01 significance level? 


7. Is this significant at the .01 significance level? 


8. Would you keep the X1 variable in your model? 


9. What is the calculated T Value for the variable X2? 


10.Is this significant at the .01 significance level? 11.Would you keep the X2 variable in your model?

Forecast

12.Forecast the number of six-packs sold if the high temperature is 60 degrees and the traffic count is 500 people.

13.Forecast the number of six-packs sold if the high temperature is 90 degrees and the traffic count is 500 people.

14.What is the average increase in six-packs of beer sold for an increase of one degree in high temperature?

Use the following to answer question 15.

15. Do the errors (residuals) violate the assumption of independent errors?

Use the following to answer question 16.

16. Does there appear to be significant autocorrelation at Lag 1?

17.The solution to the problem of autocorrelation begins with an evaluation of the model specifications. Which of the following questions should be asked? (Choose all that apply)

-Is the functional form correct?

-Were any important variables omitted?

-Are there effects that might have some pattern over time that could have introduced autocorrelation into the errors?

18.If you wanted to use dummy variables to represent the four seasons in a regression model, how many dummy variables would you need?

a–1

b–2

c–3

d–4