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(a)
Draw the time-series plot for the given data.
Identify the pattern.
(a)
![Check Mark](/static/check-mark.png)
Explanation of Solution
Step-by-step procedure to construct time-series plot is given below.
- Enter the data in columns A and B. Select the data.
- Click on Insert tab and then click on line.
- Select line with markers
The output is given below:
From the above time-series plot, it is clear that plot shows upward trend. Also, there exists seasonal pattern.
(b)
Find a multiple regression equation that represents seasonal effect using dummy variables for the given data.
(b)
![Check Mark](/static/check-mark.png)
Answer to Problem 25P
The regression equation is,
Explanation of Solution
Dummy variables are defined as given below:
Also, all the dummy variables are 0 when the reading time corresponds to 5:00 p.m. to 6:00 p.m.
The given data is entered as given below:
Hourly Dummy Variables | |||||||||||||
Date | Hour | yt | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
July 15 | 6:00 a.m. - 7:00 a.m. | 25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 15 | 7:00 a.m. - 8:00 a.m. | 28 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 15 | 8:00 a.m. - 9:00 a.m. | 35 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 15 | 9:00 a.m. - 10:00 a.m. | 50 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 15 | 10:00 a.m. - 11:00 a.m. | 60 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
July 15 | 11:00 a.m. - 12:00 p.m. | 60 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
July 15 | 12:00 p.m. - 1:00 p.m. | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
July 15 | 1:00 p.m. - 2:00 p.m. | 35 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
July 15 | 2:00 p.m. - 3:00 p.m. | 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
July 15 | 3:00 p.m. - 4:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
July 15 | 4:00 p.m. - 5:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
July 15 | 5:00 p.m. - 6:00 p.m. | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 16 | 6:00 a.m. - 7:00 a.m. | 28 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 16 | 7:00 a.m. - 8:00 a.m. | 30 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 16 | 8:00 a.m. - 9:00 a.m. | 35 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 16 | 9:00 a.m. - 10:00 a.m. | 48 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 16 | 10:00 a.m. - 11:00 a.m. | 60 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
July 16 | 11:00 a.m. - 12:00 p.m. | 65 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
July 16 | 12:00 p.m. - 1:00 p.m. | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
July 16 | 1:00 p.m. - 2:00 p.m. | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
July 16 | 2:00 p.m. - 3:00 p.m. | 35 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
July 16 | 3:00 p.m. - 4:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
July 16 | 4:00 p.m. - 5:00 p.m. | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
July 16 | 5:00 p.m. - 6:00 p.m. | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 17 | 6:00 a.m. - 7:00 a.m. | 35 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 17 | 7:00 a.m. - 8:00 a.m. | 42 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 17 | 8:00 a.m. - 9:00 a.m. | 45 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 17 | 9:00 a.m. - 10:00 a.m. | 70 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
July 17 | 10:00 a.m. - 11:00 a.m. | 72 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
July 17 | 11:00 a.m. - 12:00 p.m. | 75 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
July 17 | 12:00 p.m. - 1:00 p.m. | 60 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
July 17 | 1:00 p.m. - 2:00 p.m. | 45 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
July 17 | 2:00 p.m. - 3:00 p.m. | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
July 17 | 3:00 p.m. - 4:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
July 17 | 4:00 p.m. - 5:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
July 17 | 5:00 p.m. - 6:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Step-by-step procedure to obtain multiple linear regression line is given below.
- Enter the data in columns A to M.
- Click on Data tab and then Data Analysis.
- Select Regression and click ok.
- In Input Y
Range select, $B$2:$B$37 and Input X Range select $C$2:$M$37 - Click Ok.
The output is given below:
From the output the regression equation is,
Here, X Variable 1 represents Hour1, X Variable 2 represents Hour2, … X variable 11 represents Hour11.
(c)
Find the estimates of the levels of nitrogen for July 18 using the model developed in part (b).
(c)
![Check Mark](/static/check-mark.png)
Explanation of Solution
From part (b), the regression equation is,
Forecast for July 18 is obtained as given below:
Hourly forecast | Calculation | |
Hour1 | 29.34 | |
Hour2 | 33.34 | |
Hour3 | 38.34 | |
Hour4 | 56 | |
Hour5 | 64 | |
Hour6 | 66.67 | |
Hour7 | 50 | |
Hour8 | 40 | |
Hour9 | 35 | |
Hour10 | 25 | |
Hour11 | 23.34 | |
Hour12 | 21.67 | 21.67 |
(d)
Construct a multiple regression equation that represents seasonal effect using dummy variables and a t variable for the given data.
(d)
![Check Mark](/static/check-mark.png)
Answer to Problem 25P
The regression equation is,
Explanation of Solution
Create a variable t such that t = 1 for hour 1 on July 15, t = 2 for hour 2 on July 2, …, t = 36 for hour 12 on July 18.
The given data is entered as given below:
Hourly Dummy Variables | ||||||||||||||
Date | Hour | yt | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | t |
July 15 | 6:00 a.m. - 7:00 a.m. | 25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
July 15 | 7:00 a.m. - 8:00 a.m. | 28 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
July 15 | 8:00 a.m. - 9:00 a.m. | 35 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
July 15 | 9:00 a.m. - 10:00 a.m. | 50 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
July 15 | 10:00 a.m. - 11:00 a.m. | 60 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
July 15 | 11:00 a.m. - 12:00 p.m. | 60 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
July 15 | 12:00 p.m. - 1:00 p.m. | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
July 15 | 1:00 p.m. - 2:00 p.m. | 35 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
July 15 | 2:00 p.m. - 3:00 p.m. | 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 9 |
July 15 | 3:00 p.m. - 4:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 10 |
July 15 | 4:00 p.m. - 5:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 11 |
July 15 | 5:00 p.m. - 6:00 p.m. | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
July 16 | 6:00 a.m. - 7:00 a.m. | 28 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 |
July 16 | 7:00 a.m. - 8:00 a.m. | 30 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 |
July 16 | 8:00 a.m. - 9:00 a.m. | 35 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 |
July 16 | 9:00 a.m. - 10:00 a.m. | 48 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
July 16 | 10:00 a.m. - 11:00 a.m. | 60 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 17 |
July 16 | 11:00 a.m. - 12:00 p.m. | 65 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 18 |
July 16 | 12:00 p.m. - 1:00 p.m. | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 19 |
July 16 | 1:00 p.m. - 2:00 p.m. | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 20 |
July 16 | 2:00 p.m. - 3:00 p.m. | 35 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 21 |
July 16 | 3:00 p.m. - 4:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 22 |
July 16 | 4:00 p.m. - 5:00 p.m. | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 23 |
July 16 | 5:00 p.m. - 6:00 p.m. | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 |
July 17 | 6:00 a.m. - 7:00 a.m. | 35 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 |
July 17 | 7:00 a.m. - 8:00 a.m. | 42 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 |
July 17 | 8:00 a.m. - 9:00 a.m. | 45 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 |
July 17 | 9:00 a.m. - 10:00 a.m. | 70 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
July 17 | 10:00 a.m. - 11:00 a.m. | 72 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 29 |
July 17 | 11:00 a.m. - 12:00 p.m. | 75 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 30 |
July 17 | 12:00 p.m. - 1:00 p.m. | 60 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 31 |
July 17 | 1:00 p.m. - 2:00 p.m. | 45 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 32 |
July 17 | 2:00 p.m. - 3:00 p.m. | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 33 |
July 17 | 3:00 p.m. - 4:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 34 |
July 17 | 4:00 p.m. - 5:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 35 |
July 17 | 5:00 p.m. - 6:00 p.m. | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 |
Step-by-step procedure to obtain multiple linear regression line is given below.
- Enter the data in columns A to N.
- Click on Data tab and then Data Analysis.
- Select Regression and click ok.
- In Input Y Range select, $B$2:$B$37 and Input X Range select $C$2:$N$37
- Click Ok.
The output is given below:
From the output the regression equation is,
Here, X Variable 1 represents Hour1, X Variable 2 represents Hour2,… X variable 11 represents Hour11 and X variable 12 represents t.
(e)
Calculate the estimates of the levels of nitrogen for July 18 using the model developed in part (d).
(e)
![Check Mark](/static/check-mark.png)
Explanation of Solution
From part (d), the regression equation is,
Forecast for July 18 is given below:
Hourly forecast | T | Calculation | |
1 | 37 | 39.93 | |
2 | 38 | 43.93 | |
3 | 39 | 48.93 | |
4 | 40 | 66.6 | |
5 | 41 | 74.71 | |
6 | 42 | 77.28 | |
7 | 43 | 60.61 | |
8 | 44 | 50.61 | |
9 | 45 | 45.62 | |
10 | 46 | 35.62 | |
11 | 47 | 33.95 | |
12 | 48 | 32.29 |
(f)
Justify which of the models (b) or (d) is effective.
(f)
![Check Mark](/static/check-mark.png)
Answer to Problem 25P
Model (d) is preferred.
Explanation of Solution
For the multiple regression equation developed in part (b), MSE is obtained as given below:
Date | Hour | yt | Forecast | Forecast Error | Squared Forecast Error |
15-Jul | 6:00 a.m. - 7:00 a.m. | 25 | 29.34 | -4.34 | 18.8356 |
15-Jul | 7:00 a.m. - 8:00 a.m. | 28 | 33.34 | -5.34 | 28.5156 |
15-Jul | 8:00 a.m. - 9:00 a.m. | 35 | 38.34 | -3.34 | 11.1556 |
15-Jul | 9:00 a.m. - 10:00 a.m. | 50 | 56 | -6 | 36 |
15-Jul | 10:00 a.m. - 11:00 a.m. | 60 | 64 | -4 | 16 |
15-Jul | 11:00 a.m. - 12:00 p.m. | 60 | 66.67 | -6.67 | 44.4889 |
15-Jul | 12:00 p.m. - 1:00 p.m. | 40 | 50 | -10 | 100 |
15-Jul | 1:00 p.m. - 2:00 p.m. | 35 | 40 | -5 | 25 |
15-Jul | 2:00 p.m. - 3:00 p.m. | 30 | 35 | -5 | 25 |
15-Jul | 3:00 p.m. - 4:00 p.m. | 25 | 25 | 0 | 0 |
15-Jul | 4:00 p.m. - 5:00 p.m. | 25 | 23.34 | 1.66 | 2.7556 |
15-Jul | 5:00 p.m. - 6:00 p.m. | 20 | 21.67 | -1.67 | 2.7889 |
16-Jul | 6:00 a.m. - 7:00 a.m. | 28 | 29.34 | -1.34 | 1.7956 |
16-Jul | 7:00 a.m. - 8:00 a.m. | 30 | 33.34 | -3.34 | 11.1556 |
16-Jul | 8:00 a.m. - 9:00 a.m. | 35 | 38.34 | -3.34 | 11.1556 |
16-Jul | 9:00 a.m. - 10:00 a.m. | 48 | 56 | -8 | 64 |
16-Jul | 10:00 a.m. - 11:00 a.m. | 60 | 64 | -4 | 16 |
16-Jul | 11:00 a.m. - 12:00 p.m. | 65 | 66.67 | -1.67 | 2.7889 |
16-Jul | 12:00 p.m. - 1:00 p.m. | 50 | 50 | 0 | 0 |
16-Jul | 1:00 p.m. - 2:00 p.m. | 40 | 40 | 0 | 0 |
16-Jul | 2:00 p.m. - 3:00 p.m. | 35 | 35 | 0 | 0 |
16-Jul | 3:00 p.m. - 4:00 p.m. | 25 | 25 | 0 | 0 |
16-Jul | 4:00 p.m. - 5:00 p.m. | 20 | 23.34 | -3.34 | 11.1556 |
16-Jul | 5:00 p.m. - 6:00 p.m. | 20 | 21.67 | -1.67 | 2.7889 |
17-Jul | 6:00 a.m. - 7:00 a.m. | 35 | 29.34 | 5.66 | 32.0356 |
17-Jul | 7:00 a.m. - 8:00 a.m. | 42 | 33.34 | 8.66 | 74.9956 |
17-Jul | 8:00 a.m. - 9:00 a.m. | 45 | 38.34 | 6.66 | 44.3556 |
17-Jul | 9:00 a.m. - 10:00 a.m. | 70 | 56 | 14 | 196 |
17-Jul | 10:00 a.m. - 11:00 a.m. | 72 | 64 | 8 | 64 |
17-Jul | 11:00 a.m. - 12:00 p.m. | 75 | 66.67 | 8.33 | 69.3889 |
17-Jul | 12:00 p.m. - 1:00 p.m. | 60 | 50 | 10 | 100 |
17-Jul | 1:00 p.m. - 2:00 p.m. | 45 | 40 | 5 | 25 |
17-Jul | 2:00 p.m. - 3:00 p.m. | 40 | 35 | 5 | 25 |
17-Jul | 3:00 p.m. - 4:00 p.m. | 25 | 25 | 0 | 0 |
17-Jul | 4:00 p.m. - 5:00 p.m. | 25 | 23.34 | 1.66 | 2.7556 |
17-Jul | 5:00 p.m. - 6:00 p.m. | 25 | 21.67 | 3.33 | 11.0889 |
1076.001 |
For the multiple regression equation developed in part (d), MSE is obtained as given below:
Date | Hour | t | yt | Forecast | Forecast Error | Squared Forecast Error |
15-Jul | 6:00 a.m. - 7:00 a.m. | 1 | 25 | 24.09 | 0.91 | 0.8281 |
15-Jul | 7:00 a.m. - 8:00 a.m. | 2 | 28 | 28.09 | -0.09 | 0.0081 |
15-Jul | 8:00 a.m. - 9:00 a.m. | 3 | 35 | 33.09 | 1.91 | 3.6481 |
15-Jul | 9:00 a.m. - 10:00 a.m. | 4 | 50 | 50.76 | -0.76 | 0.5776 |
15-Jul | 10:00 a.m. - 11:00 a.m. | 5 | 60 | 58.87 | 1.13 | 1.2769 |
15-Jul | 11:00 a.m. - 12:00 p.m. | 6 | 60 | 61.44 | -1.44 | 2.0736 |
15-Jul | 12:00 p.m. - 1:00 p.m. | 7 | 40 | 44.77 | -4.77 | 22.7529 |
15-Jul | 1:00 p.m. - 2:00 p.m. | 8 | 35 | 34.77 | 0.23 | 0.0529 |
15-Jul | 2:00 p.m. - 3:00 p.m. | 9 | 30 | 29.78 | 0.22 | 0.0484 |
15-Jul | 3:00 p.m. - 4:00 p.m. | 10 | 25 | 19.78 | 5.22 | 27.2484 |
15-Jul | 4:00 p.m. - 5:00 p.m. | 11 | 25 | 18.11 | 6.89 | 47.4721 |
15-Jul | 5:00 p.m. - 6:00 p.m. | 12 | 20 | 16.45 | 3.55 | 12.6025 |
16-Jul | 6:00 a.m. - 7:00 a.m. | 13 | 28 | 29.37 | -1.37 | 1.8769 |
16-Jul | 7:00 a.m. - 8:00 a.m. | 14 | 30 | 33.37 | -3.37 | 11.3569 |
16-Jul | 8:00 a.m. - 9:00 a.m. | 15 | 35 | 38.37 | -3.37 | 11.3569 |
16-Jul | 9:00 a.m. - 10:00 a.m. | 16 | 48 | 56.04 | -8.04 | 64.6416 |
16-Jul | 10:00 a.m. - 11:00 a.m. | 17 | 60 | 64.15 | -4.15 | 17.2225 |
16-Jul | 11:00 a.m. - 12:00 p.m. | 18 | 65 | 66.72 | -1.72 | 2.9584 |
16-Jul | 12:00 p.m. - 1:00 p.m. | 19 | 50 | 50.05 | -0.05 | 0.0025 |
16-Jul | 1:00 p.m. - 2:00 p.m. | 20 | 40 | 40.05 | -0.05 | 0.0025 |
16-Jul | 2:00 p.m. - 3:00 p.m. | 21 | 35 | 35.06 | -0.06 | 0.0036 |
16-Jul | 3:00 p.m. - 4:00 p.m. | 22 | 25 | 25.06 | -0.06 | 0.0036 |
16-Jul | 4:00 p.m. - 5:00 p.m. | 23 | 20 | 23.39 | -3.39 | 11.4921 |
16-Jul | 5:00 p.m. - 6:00 p.m. | 24 | 20 | 21.73 | -1.73 | 2.9929 |
17-Jul | 6:00 a.m. - 7:00 a.m. | 25 | 35 | 34.65 | 0.35 | 0.1225 |
17-Jul | 7:00 a.m. - 8:00 a.m. | 26 | 42 | 38.65 | 3.35 | 11.2225 |
17-Jul | 8:00 a.m. - 9:00 a.m. | 27 | 45 | 43.65 | 1.35 | 1.8225 |
17-Jul | 9:00 a.m. - 10:00 a.m. | 28 | 70 | 61.32 | 8.68 | 75.3424 |
17-Jul | 10:00 a.m. - 11:00 a.m. | 29 | 72 | 69.43 | 2.57 | 6.6049 |
17-Jul | 11:00 a.m. - 12:00 p.m. | 30 | 75 | 72 | 3 | 9 |
17-Jul | 12:00 p.m. - 1:00 p.m. | 31 | 60 | 55.33 | 4.67 | 21.8089 |
17-Jul | 1:00 p.m. - 2:00 p.m. | 32 | 45 | 45.33 | -0.33 | 0.1089 |
17-Jul | 2:00 p.m. - 3:00 p.m. | 33 | 40 | 40.34 | -0.34 | 0.1156 |
17-Jul | 3:00 p.m. - 4:00 p.m. | 34 | 25 | 30.34 | -5.34 | 28.5156 |
17-Jul | 4:00 p.m. - 5:00 p.m. | 35 | 25 | 28.67 | -3.67 | 13.4689 |
17-Jul | 5:00 p.m. - 6:00 p.m. | 36 | 25 | 27.01 | -2.01 | 4.0401 |
414.6728 |
MSE for model in (d) is smaller than MSE for the model in (b). Thus, model (d) is preferred.
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Chapter 8 Solutions
Essentials of Business Analytics (MindTap Course List)
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