Reference no: EM131421889
Assignment: Mountain Bike Sales Forecasting
Note: Forecast the mountain bike sales
The first question requires you to draw a scatter plot on Excel.
The quarterly sales of the TRK-50 mountain bike for the previous four years by a bicycle shop in Switzerland is presented in the Table below:
|
Year
|
Quarter
|
Q=Sales
|
|
2010
|
1
|
10
|
|
|
2
|
31
|
|
|
3
|
43
|
|
|
4
|
16
|
|
2011
|
1
|
11
|
|
|
2
|
33
|
|
|
3
|
45
|
|
|
4
|
17
|
|
2012
|
1
|
13
|
|
|
2
|
34
|
|
|
3
|
48
|
|
|
4
|
19
|
|
2013
|
1
|
15
|
|
|
2
|
37
|
|
|
3
|
51
|
|
|
4
|
21
|
Plot these sales versus time.
Use the data above to estimate the monthly trend in sales using a linear trend model of the form: Q_t = a + bt. Does your statistical analysis indicate a trend? If so, is it an upward or downward trend and how great is it? Is it a statistically significant trend (use the 5 percent level of significance)?
Now adjust your statistical model to account for seasonal variation in sales. Estimate the following model of sales:
Q_t = a + bt + cD_2 + dD_3 + eD_4
where D_2 , D_3 ,D_4 are the appropriately defined dummy variables for quarters 2,3 and 4.
Do the data indicate a statistically significant seasonal pattern (use the 5 percent level of significance)? If so, what is the seasonal pattern of sales?
From the computer output do all independent variables seem to be important? Justify your answer statistically.
Comparing your estimates of the trend in sales in parts b and c, which estimate is likely to be more accurate? Why?
Using the estimated forecast equation from part c, forecast sales for Quarter 1 and 4, 2014 , Quarter 2 and 3 , 2015.