Sales data for two years are as follows. Data are aggregated with two months of sales (in 1,000 units) in each "period."
Year 1
|
Year 2
|
Period
|
Sales
|
Period
|
Sales
|
January-February
|
115
|
January-February
|
124
|
March-April
|
112
|
March-April
|
132
|
May-June
|
159
|
May-June
|
168
|
July-August
|
182
|
July-August
|
203
|
September-October
|
126
|
September-October
|
135
|
November-December
|
106
|
November-December
|
123
|
A) Plot the data.
B) Fit a linear regression model to the sales data.
C) In addition to the regression model, determine multiplicative seasonal index factors. A full cycle is assumed to be a full year.
D) Using the results from parts b) and c), prepare a forecast for the next year.