This is shown in table 1.
Table 1; Calculations to create the index
1
2
3
4
Month
Average
Year 1
Year 2
Year 3
Year 4
1
39,600
0.45
1.14
1.51
0.90
2
37,080
0.53
1.25
0.83
1.38
3
30,000
0.52
0.74
1.59
1.15
4
59,210
0.91
0.70
1.25
1.15
5
64,375
1.29
0.71
0.94
1.06
6
57,750
1.26
0.72
0.96
1.06
7
47,370
1.17
0.84
0.68
1.32
8
56,638
1.01
1.13
0.68
1.17
9
29,855
0.52
1.59
0.84
1.05
10
39,638
0.70
1.09
1.29
0.92
11
27,323
0.78
1.44
1.16
0.61
12
19,350
0.88
0.53
1.61
0.98
With the creation of the index for each moth, this may then be used to assess the most likely demand. The most appropriate method is the use of the least square regression. This uses the data from the previous years and places them on a graph, drawing a straight line through the points so it has the least distance from the different points. The future forecasts are assumed to be on this line. The equation for the line can be used to calculate forecasts. The graph for month 1 is shown below in figure 1.
Figure 2; Graph for Month 1 demand
The line shows the general trend and is the closest the line can be drawn to all...
This is demonstrated in figure 1, with the data shown and the regression line shown. The equation for the line can be used to forecast the demand for any point in the future. Figure 1; Chart for month 1 with forcast and regression line The line shown has the equation y = 0.0289x + 0.9278, where x is the year, so for year 5 the equation would be (0.0289x 5) +
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