Consider the following time series graphs of monthly motor oil demand for your f
ID: 455794 • Letter: C
Question
Consider the following time series graphs of monthly motor oil demand for your firm's truck fleet (in gallons). Given this information, which approach below would you consider to be the best approach to modeling this problem?
Create indicator (dummy) variables for each period of seasonal variation on the graph and a variable for time period. Then using all but one of the seasonal variables and time period as the independent variables, use linear regression to forecast motor oil demand.
Create a single seasonal variable for motor oil demand that consists of an integer for each period of seasonal variation on the graph, and use this variable in a regression model to forecast motor oil demand.
Forecast motor oil demand using a moving average with 6 periods.
Using time period as an independent variable, create a trend model using linear regression to forecast motor oil demand.
Forecast motor oil demand using exponential smoothing with alpha = 0.2.
Forecast motor oil demand using a three period moving average.
Forecast motor oil demand using exponential smoothing with alpha = 0.5.
a.Create indicator (dummy) variables for each period of seasonal variation on the graph and a variable for time period. Then using all but one of the seasonal variables and time period as the independent variables, use linear regression to forecast motor oil demand.
b.Create a single seasonal variable for motor oil demand that consists of an integer for each period of seasonal variation on the graph, and use this variable in a regression model to forecast motor oil demand.
c.Forecast motor oil demand using a moving average with 6 periods.
d.Using time period as an independent variable, create a trend model using linear regression to forecast motor oil demand.
e.Forecast motor oil demand using exponential smoothing with alpha = 0.2.
f.Forecast motor oil demand using a three period moving average.
g.Forecast motor oil demand using exponential smoothing with alpha = 0.5.
350 1000 900 300 800 700 2014 2013 2012 500 400 300 200 100 0 200 150 100 Jan Feb Mar Apr May Jun Ag Sep Odt Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecExplanation / Answer
Create indicator (dummy) variables for each period of seasonal variation on the graph and a variable for time period. Then using all but one of the seasonal variables and time period as the independent variables, use linear regression to forecast motor oil demand