Criticism can be made regarding the forecasting method that involves using new subscriptions data from the previous three months to project future numbers. This approach may result in an inaccurate representation as it fails to consider the cyclical nature of illnesses. For example, during winter, the flu virus may cause more people to fall ill. In spring and autumn, there may be an increase in allergy-related symptoms reported. Additionally, when the weather is favorable, more individuals may engage in outdoor activities, leading to sports injuries. The summer season can bring about issues such as swimmer's ear or sunburns. Thus, it is important to examine a full year's worth of data and account for seasonal effects.
Apart from the number of telemarketing hours spent, there are various other factors that can be useful in predicting the
...number of new subscriptions. The price of the drugs may significantly influence the number of prescriptions filled. Furthermore, factors such as store location, including foot traffic and proximity to competitors, can play a role. The convenience of the store's location (e.g., being on the right side of the street for easy traffic flow) can also affect numbers. Competitors' prices may impact subscription rates as well. Additionally, seasonal illnesses or injuries should be taken into consideration. Other marketing techniques like TV, radio, and newspaper advertisements that create awareness about the drugs may also have an effect.
The drug type and its target population are also crucial. For instance, if the drug is for heart disease, it is relevant for the elderly population. If it is for sickle cell disease, it is relevant for African American children. Moreover, if the drug is
for breast cancer, it is predominantly for women.
3.A) The regression equation derived from the excel regression analysis is as follows: 413.82 + 4.4. It is noteworthy that the F value and the P value for X are significant, indicating that the model can be used to predict Y.
B) Assuming that we allocate 1200 hours to telemarketing, the expected number of new subscriptions would be: = -413.82 + 4.(1200) = 4866.
C) It is important to note that 2000 telemarketing hours fall outside the relevant range for the data. The minimum and maximum numbers of telemarketing hours were 704 and 1498, respectively. Extrapolating beyond this range can lead to inaccurate predictions since the relationship may not be linear as marketing hours increase. There might be diminishing returns after reaching a certain number of telemarketing hours. Predicting 2000 telemarketing hours using this model may result in an overestimation of the number of new subscriptions.
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