Problem #2a: The following dummy variables were used to compute the multiple regression equation to account for any seasonal effects in the data: Qrt1 = 1 if Quarter 1, 0 otherwise; Qrt2 = 1 if Quarter 2, O otherwise; Qrt3 = 1 if Quarter 3, 0 otherwise. %3D SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.9897 0.9796 0.9719 Standard Error 124.9667 Observations 12 ANOVA df SS MS F Regressi on Significance F 3. 5996941.67 1998980.56 128.00 Residual 0.00000042 8 124933.33 15616.67 Total 11 6121875.00 Coefficients Standard Error t Stat P-value Intercept 2491.67 72.150 34.53 0.0000000005 Qtrl -711.67 102.035 -6.97 0.000115518 Qtr2 -1511.67 102.035 -14.82 0.00000042 Qtr3 326.67 102.035 3.20 0,012583566 Using the above Excel output, compute the 4 quarterly forecasts for next year.
Problem #2a: The following dummy variables were used to compute the multiple regression equation to account for any seasonal effects in the data: Qrt1 = 1 if Quarter 1, 0 otherwise; Qrt2 = 1 if Quarter 2, O otherwise; Qrt3 = 1 if Quarter 3, 0 otherwise. %3D SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.9897 0.9796 0.9719 Standard Error 124.9667 Observations 12 ANOVA df SS MS F Regressi on Significance F 3. 5996941.67 1998980.56 128.00 Residual 0.00000042 8 124933.33 15616.67 Total 11 6121875.00 Coefficients Standard Error t Stat P-value Intercept 2491.67 72.150 34.53 0.0000000005 Qtrl -711.67 102.035 -6.97 0.000115518 Qtr2 -1511.67 102.035 -14.82 0.00000042 Qtr3 326.67 102.035 3.20 0,012583566 Using the above Excel output, compute the 4 quarterly forecasts for next year.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question

Transcribed Image Text:Problem #2a:
The following dummy variables were used to compute the multiple regression
equation to account for any seasonal effects in the data: Qrt1 = 1 if Quarter 1, 0
otherwise; Qrt2 = 1 if Quarter 2, O otherwise; Qrt3 = 1 if Quarter 3, 0 otherwise.
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
0.9897
0.9796
Adjusted R Square
0.9719
Standard Error
124.9667
Observations
12
ANOVA
df
SS
MS
Significance F
F
Regressi on
3
5996941.67
1998980.56
128.00
0.00000042
Residual
8.
124933.33
15616.67
Total
11
6121875.00
Coefficients
Standard Error
t Stat
P-value
Intercept
2491.67
72.150
34.53
0.0000000005
Qtrl
-711.67
102.035
-6.97
0.000115518
Qtr2
-1511.67
102.035
-14.82
0.00000042
Qtr3
326.67
102.035
3.20
0,012583566
Using the above Excel output, compute the 4 quarterly forecasts for next year.

Transcribed Image Text:Problem #2:
The quarterly sales data (number of copies sold) for a college textbook over the
past three years follow.
Quarter Year 1 Year 2 Year 3
1
1690
1800
1850
2
940
900
1100
2625
2900 2930
4
2500
2360
2615
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