The following table shows the average monthly distance traveled (in billion miles) by vehicles on urban highways for five different years. Urban Highways - Average Monthly Distance Traveled by Vehicles (Billion Miles) Years Jan Year 1 4.22 Year 2 4.31 Year 3 4.38 Year 4 4.45 Year 5 4.51 Apr May 4.92 4.49 4.98 4.59 Feb Mar Jun July 4.55 Aug Sep 4.44 Oct Nov Dec 4.39 4.37 4.35 4.79 5.32 5.21 5.12 4.49 5.44 5.34 5.24 4.68 4.65 4.61 4.68 4.74 5.51 4.88 5.41 5.5 5.36 4.98 4.63 4.71 4.78 4.82 4.85 4.89 5.59 5.41 4.82 5.01 5.12 4.72 4.78 4.79 4.92 5.06 5.11 5.65 5.62 5.49 4.8 4.88 4.82 4.95 5.12 5.22 5.44 a. Construct a time series plot. What type of pattern exists in the data? b. Use a multiple regression model to develop an equation to account for seasonal effects and any linear trend in the data. To capture seasonal effects, use the dummy variables Jan = 1 if month is January, 0 otherwise; Feb = 1 if month is February, 0 otherwise; ...; Nov = 1 if month is November, 0 otherwise; and create a variable t such that 1= 1 for January of year 1, 1 = 2 for February of year 1, .., 1= 60 for December of year 5. c. Compute the forecast (in billion miles) for the next three months based on the model developed in part a.

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The following table shows the average monthly distance traveled (in billion miles) by vehicles on urban highways for five
different years.
Urban Highways - Average Monthly Distance Traveled by Vehicles (Billion Miles)
Years Jan
Year 1 4.22
Year 2 4.31
Year 3 4.38
Year 4 4.45
Year 5 4.51
Feb
Mar Apr May Jun
July Aug Sep
Oct
Nov
Dec
5.32
5.21
5.12
4.92
4.49
4.55
4.49
4.44| 4.39
4.37 4.35
4.68 | 4.74 | 4.79
4.88
4.92
5.44
5.34
5.24
4.98
4.59
4.68
4.65
4.61
5.51
5.36
4.63
4.85 4.89
4.98
5.01
5.41
4.71
4.78
4.82
5.59
5.5
5.41
4.72
4.78
4.79
4.82
5.06
5.11
5.65
5.62
5.49
5.12
4.8
4.88
4.82
4.95
5.12
5.22 5.44
a. Construct a time series plot. What type of pattern exists in the data?
b. Use a multiple regression model to develop an equation to account for seasonal effects and any linear trend in the data.
To capture seasonal effects, use the dummy variables Jan = 1 if month is January, 0 otherwise; Feb = 1 if month is
February, 0 otherwise; ...; Nov = 1 if month is November, 0 otherwise; and create a variable t such that f = 1 for January
of year 1, 1 = 2 for February of year 1, ..., t = 60 for December of year 5.
c. Compute the forecast (in billion miles) for the next three months based on the model developed in part a.
Transcribed Image Text:The following table shows the average monthly distance traveled (in billion miles) by vehicles on urban highways for five different years. Urban Highways - Average Monthly Distance Traveled by Vehicles (Billion Miles) Years Jan Year 1 4.22 Year 2 4.31 Year 3 4.38 Year 4 4.45 Year 5 4.51 Feb Mar Apr May Jun July Aug Sep Oct Nov Dec 5.32 5.21 5.12 4.92 4.49 4.55 4.49 4.44| 4.39 4.37 4.35 4.68 | 4.74 | 4.79 4.88 4.92 5.44 5.34 5.24 4.98 4.59 4.68 4.65 4.61 5.51 5.36 4.63 4.85 4.89 4.98 5.01 5.41 4.71 4.78 4.82 5.59 5.5 5.41 4.72 4.78 4.79 4.82 5.06 5.11 5.65 5.62 5.49 5.12 4.8 4.88 4.82 4.95 5.12 5.22 5.44 a. Construct a time series plot. What type of pattern exists in the data? b. Use a multiple regression model to develop an equation to account for seasonal effects and any linear trend in the data. To capture seasonal effects, use the dummy variables Jan = 1 if month is January, 0 otherwise; Feb = 1 if month is February, 0 otherwise; ...; Nov = 1 if month is November, 0 otherwise; and create a variable t such that f = 1 for January of year 1, 1 = 2 for February of year 1, ..., t = 60 for December of year 5. c. Compute the forecast (in billion miles) for the next three months based on the model developed in part a.
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