What is the expected value at node 4 a 4750 b 4375 c

13. What is the expected value at node 4?
A. 47.50
B. 43.75
C. 70.00
D. 45.00
E. 71.25
14. What is the expected value of perfect information?
15. What is the expected value at node 2?
16. What is the best decision strategy for the manager?
17. What is the expected value of sample information?
A. 45.00
B. 71.25
C. 49.50
D. 1.25
E. 4.50
6

18. A statistician working for a car manufacturer developed a statistical model for
predicting delivery time (the number of days between ordering a car and actual
delivery) of a particular model for which there is a range of factory-fitted options. Use
the output below to forecast the difference in delivery times for cars with 6 options and
9 options, rounded to two decimal places.
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.92
R Square 0.85
Adjusted R Square 0.83
Standard Error 4.38
Observations 10
Analysis of Variance
df SS
Regression 1 890.19
Residual 8 153.41
Total 9 1043.59
Coefficients Standard Error
Intercept 29.73 2.99
Factory-Fitted Options 3.28 0.48
7

19. Given the table below what is the MAD and the MSE?
Period Actual Forecast
1 95 100
2 108 110
3 123 120
4 130 130
MAD = ___________________ MSE = ___________________________
20. The Acme Computer Company has recorded sales of one of its products for a six-week period:
Using the three-week simple moving-average method, forecast sales for week 7.
21. Shown below are data that reflect the number of daily traffic accidents at a dangerous city
intersection. The regression equation is: number of accidents = 5.3 + 0.5 t
What is the forecasted number of accidents for day 6?
Day (t) number of accidents
1 5
2 7
3 8
4 6
5 8
8

22. The following table contains the number of consumer complaints received in a Publix supermarket
in Florida. Use exponential smoothing with a constant of α = 0.33 to forecast the number of complaints
in March, round to the nearest whole number of complaints.
Month Number of Complaints
January 36
February 45
March 81
April 90
May 108
June 144
A. 39
B. 42
C. 45
D. 53
E. 64
For the next 3 questions, consider the following summary output from Microsoft Excel for a simple
regression of Halliburton (ticker symbol HAL) weekly stock prices on the S&P500 stock index, for the
period January 2004 through August 2006.
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.93
R Square 0.87
Adjusted R
Square
0.86
Standard Error 3.20
Observations 140
ANOVA
Df SS MS F Significanc
e F
Regression 1 9067.88 9067.8
9
884.90 6.84E-62
Residual 138 1414.13 10.24
Total 139 10482.0
Coefficient
s
Standard
Error
t Stat P-value Lower 95% Upper
95%
Intercept -121.65 4.914 -24.74 1.37E-
52
-131.37 -111.93
S&P500 Index 0.122 0.004 29.75
6.84E-
62
0.11 0.13