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 the following:
a. Construct a causal regression model using PMI as the causal variable. How well does your model fit the data? The R2 shows that 65% of the variance in sales is explained by the PMI.
b. Suppose that the 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? This new model is better because it explains 91% of the variance in sales.
c. Pick the best model from parts a. and b, and create a forecast if the PMI is 47.3.

  • CreatedMarch 30, 2015
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