solution
Many supply managers use a monthly reported survey result known as the purchasing managers’ index (PMI) as a leading indicator to forecast future sales for their businesses. Suppose that the PMI and your business sales data for the last 10 months are as follows:
|
Month |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
|
PMI |
42.1 |
43.0 |
41.0 |
38.2 |
40.2 |
44.1 |
45.8 |
49.0 |
48.7 |
52.0 |
|
Sales (1,000’s) |
121 |
123 |
125 |
120 |
118 |
118 |
122 |
127 |
135 |
136 |
- Construct a casual regression model using PMI as the casual variable. How well does your model fit the data?
- Suppose that PMI is truly a leading indicator. That is, the PMI value in one period influences sales in the following period. Construct a new regression model using this information. Is the new model better or worse than the model you made for part (a)?
- Pick the best model from parts (a) and (b), create a forecast for sales given PMI = 47.3.
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