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Case Study 13.3

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Submitted By troublechange
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1. What criticism can you make concerning the method of forecasting that involved taking the new subscriptions data for the prior three months as the basis for future projections?
Taking the past three months of data and projecting it to the whole year can lead to an inaccurate picture because it doesn’t take into consideration the differentiation caused by different times of the year. Each season brings out a different set of adventurers, you wouldn’t market snowboards in the middle of the summer, or allergy medication in the middle of winter. But the forecasting data even admits to relying on data submitted during a time when training is taking place. If there is not time invested, the results will not accurately provide the effects of the circumstance. 2. What factors other than number of telemarketing hours spent might be useful in predicting the number of new subscriptions? Explain

The price may play a significant role in the number of subscriptions. If a special is being run in June for half the price it was run for in May, there will be an increase in subscriptions, it the market is accurately approached. So can the location of the stores in which the trial subscriptions are being delivered. It the trial subscriptions are submitted in an area in which the Harold is not known or recognized this is less likely for the trials to be acknowledged and followed through to full subscriptions, if a similar subscription is being run at half of the price, it is likely the number of subscriptions of the Harold will drop. 3. A. Regression Model
Subscriptions= -413.82+4.40795X
For each increased marketing hour, we can expect 4.40709 new subscriptions.
The r2 value is 0,8617 meaning there is a 86.17% of the variation in the subscriptions is explained by the telemarketing.
B. =-413.82+4.40795(1200) =4,875.72 This is not a good representation of the

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