Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: 

Hour1 = 1 if the reading was made between 6:00 A.M. and 7:00A.M.; 0 otherwise 

Hour2 = 1 if the reading was made between 7:00 A.M. and 8:00 A.M.; 0 otherwise 

. 

. 

. 

Hour11 = 1 if the reading was made between 4:00 P.M. and 5:00 P.M., 0 otherwise 

Note that when the values of the 11 dummy variables are equal to 0, the observation corresponds to the 5:00 P.M. to 6:00 P.M. hour. 

If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: 300) Do not round intermediate calculation. 

Value = + Hour1 + Hour2 + Hour3 + Hour4 + Hour5 + Hour6 + Hour7 + Hour8 + Hour9 + Hour10 + Hour11 


(c) 
Using the equation developed in part (b), compute estimates of the levels of nitrogen dioxide for July 18. 

If required, round your answers to three decimal places. Do not round intermediate calculation. 

6:00 a.m. – 7:00 a.m. forecast 

7:00 a.m. – 8:00 a.m. forecast 

8:00 a.m. – 9:00 a.m. forecast 

9:00 a.m. – 10:00 a.m. forecast 

10:00 a.m. – 11:00 a.m. forecast 

11:00 a.m. – noon forecast 

noon – 1:00 p.m. forecast 

1:00 p.m. – 2:00 p.m. forecast 

2:00 p.m. – 3:00 p.m. forecast 

3:00 p.m. – 4:00 p.m. forecast 

4:00 p.m. – 5:00 p.m. forecast 

5:00 p.m. – 6:00 p.m. forecast 




(d) 
Let t = 1 to refer to the observation in hour 1 on July 15; t = 2 to refer to the observation in hour 2 of July 15; …; and t = 36 to refer to the observation in hour 12 of July 17. Using the dummy variables defined in part (b) and t_{s}, develop an equation to account for seasonal effects and any linear trend in the time series. 

If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: 300) 

Value = + Hour1 + Hour2 + Hour3 + Hour4 + Hour5 + Hour6 + Hour7 + Hour8 + Hour9 + Hour10 + Hour11 + t 


(e) 
Based on the seasonal effects in the data and linear trend estimated in part (d), compute estimates of the levels of nitrogen dioxide for July 18. 

If required, round your answers to three decimal places. 

6:00 a.m. – 7:00 a.m. forecast 

7:00 a.m. – 8:00 a.m. forecast 

8:00 a.m. – 9:00 a.m. forecast 

9:00 a.m. – 10:00 a.m. forecast 

10:00 a.m. – 11:00 a.m. forecast 

11:00 a.m. – noon forecast 

noon – 1:00 p.m. forecast 

1:00 p.m. – 2:00 p.m. forecast 

2:00 p.m. – 3:00 p.m. forecast 

3:00 p.m. – 4:00 p.m. forecast 

4:00 p.m. – 5:00 p.m. forecast 

5:00 p.m. – 6:00 p.m. forecast 




(f) 
Is the model you developed in part (b) or the model you developed in part (d) more effective? 

If required, round your answers to three decimal places. 


Model developed in part (b) 
Model developed in part (d) 
MSE 




– Select your answer Model developed in part (b)Model developed in part (d)Item 54 