Air pollution control specialists in southern California monitor the amount of ozone, carbon dioxide, and nitrogen dioxide in the air on an hourly basis. The hourly time series data exhibit seasonality, with the levels of pollutants showing patterns that vary over the hours in the day. On July 15, 16, and 17, the following levels of nitrogen dioxide were observed for the 12 hours from 6:00 A. M. to 6:00 P. M.

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)

Value=21.7 +7.67 hour1+11.7 hour2+16.7 hour3+34.3 hour4+42.3 hour5+45.0 hour6+28.3 hour7+18.3 hour8+13.3 hour9+3.33 hour10+1.67 hour11

6:00 a. m. - 7:00 a. m. forecast

29.31

7:00 a. m. - 8:00 a. m. forecast

33.4

8:00 a. m. - 9:00 a. m. forecast

38.4

9:00 a. m. - 10:00 a. m. forecast

56

10:00 a. m. - 11:00 a. m. forecast

64

11:00 a. m. - noon forecast

66.7

noon - 1:00 p. m. forecast

50

1:00 p. m. - 2:00 p. m. forecast

40

2:00 p. m. - 3:00 p. m. forecast

35

3:00 p. m. - 4:00 p. m. forecast

25.03

4:00 p. m. - 5:00 p. m. forecast

23.37

5:00 p. m. - 6:00 p. m. forecast

21.7

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 ts, 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=11.2 +12.5 hour1+16.0 hour2+20.06 hour3+37.8 hour4+45.4 hour5+47.6 hour6+30.5 hour7+20.1 hour8+14.6 hour9+4.21 hour10+2.10 hour11+0.437 t

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

6:00 a. m. - 7:00 a. m. forecast

40

7:00 a. m. - 8:00 a. m. forecast

44

8:00 a. m. - 9:00 a. m. forecast

49

9:00 a. m. - 10:00 a. m. forecast

66

10:00 a. m. - 11:00 a. m. forecast

75

11:00 a. m. - noon forecast

77

noon - 1:00 p. m. forecast

60

1:00 p. m. - 2:00 p. m. forecast

51

2:00 p. m. - 3:00 p. m. forecast

45

3:00 p. m. - 4:00 p. m. forecast

36

4:00 p. m. - 5:00 p. m. forecast

34

5:00 p. m. - 6:00 p. m. forecast

32

This is the part I cant figure out, please help me find the MSE's

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

Model developed in part (b)

Model developed in part (d)

MSE