BA Homework 6
xlsx
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School
Houston Baptist University *
*We aren’t endorsed by this school
Course
4340
Subject
Aerospace Engineering
Date
Dec 6, 2023
Type
xlsx
Pages
6
Uploaded by lululas1
PROBLEM 1 (15 POINTS)
Consider the following time series data:
Month
Value
naïve forecast absolute errorerror 2
absolute value of % error
1
24
2
13
24
11
121
85%
3
20
13
7
49
35%
4
12
20
8
64
67%
5
19
12
7
49
37%
6
23
19
4
16
17%
7
15
23
8
64
53%
15
total
45
363
2.93848794226369
mean
7.5
60.5
0.489747990377281
(a) Construct a time series plot. What type of pattern exists in the data?
(b) Compute the mean absolute error (MAE), mean squared error (MSE), and mean absolute percentage error MAPE)
using the most recent value as the forecast for the next period. What is the forecast for month 8?
MAE
MSE
MAPE
forecast
(c) Compute the MAE, MSE, and MAPE using the average of all the data as the forecast for the next period.
What is the forecast for month 8?
MAE
MSE
MAPE
forecast
(d) Develop a three-week moving average for this time series and compute the MAE, MSE, and MAPE.
What is the forecast for month 8.
Month
Value
3 week
forecast error abs
1
24
2
13
3
20
4
12
19
5
19
15
6
23
17
7
15
18
MAE
MSE
MAPE
MAE, MSE, and MAPE. What is the forecast for month 8?
(e) Compare the results. Which appears to provide the better forecast based on the MSE?
(d) Use ά = 0.20 to compute the exponential smoothing values for the time series and compute the
1
2
3
4
5
6
7
0
5
10
15
20
25
30
Chart Title
Month
Value
PROBLEM 2 (20 POINTS)
The following data shows the quarterly sales of the LukeHydro tents at a Aubrey Sporting Goods Store.
The store owner wants to forecast the quarterly sales for the next year.
(a) Construct a time series plot. What type of pattern exists in the data?
(b) Use dummy variables and multiple regression to find a multiple regression equation that accounts for seasonal effects in the data?
(c) Based on this model, estimate the quarterly sales for the fourth year.
Year
Quarter
Q1
Q2
Q3
Sales
1
1
1
0
0
74
1
2
0
1
0
53
1
3
0
0
1
61
1
4
0
0
0
82
2
1
1
0
0
71
2
2
0
1
0
45
2
3
0
0
1
65
2
4
0
0
0
87
3
1
1
0
0
67
3
2
0
1
0
57
3
3
0
0
1
58
3
4
0
0
0
78
4
1
70.666666667
4
2
51.666666667
4
3
61.333333333
4
4
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1
2
3
4
1
2
3
4
1
2
3
4
0
10
20
30
40
50
60
70
80
90
100
Chart Title
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.9506036804
R Square
0.9036473571
Adjusted R Square
0.8675151161
Standard Error
4.5368858629
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
3 1544.33333 514.777778 25.0094467 0.00020376
Residual
8 164.666667 20.5833333
Total
11
1709
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
82.333333333 2.61937227 31.4324673 1.14178E-09
76.29305 88.3736166
Q1
-11.666666667
3.7043518 -3.14944889 0.01360979 -20.2089172 -3.12441611
Q2
-30.666666667
3.7043518 -8.27855138 3.41043E-05 -39.2089172 -22.1244161
Q3
-21
3.7043518 -5.66900801 0.00047094 -29.5422506 -12.4577494
Lower 99.0% Upper 99.0%
73.5443248 91.1223419
-24.0962018 0.76286842
-43.0962018 -18.2371316
-33.4295351 -8.57046492
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