Final answer:
The scenario described uses a b. Classification Model. Classification models are used to predict behaviors like coupon usage in demarcated regions. They are different from Regression Models or OLAP, which are used for numerical predictions and business data analysis, respectively.
Step-by-step explanation:
The task described falls under the category of a Classification Model. The aim is to predict what region of town will have the most customers who will use a $5 off coupon based on their past behavioral patterns. This kind of predictive modeling is used to sort or categorize entities into different classes or regions and is not focused on predicting a numerical value, which distinguishes it from a regression model. OLAP (Online Analytical Processing) is more about multidimensional analysis of business data and does not align with making predictions about specific behaviors such as coupon usage.
Examples of when a regression model might be used include predicting sales growth or estimating the effect of a new business, like a liquor store, on local crime rates. Regression analysis can provide insights into which variables are significant predictors of an outcome. For example, using sales data to create a regression model would allow prediction of future sales based on past trends, answering questions like what the sales might be like on day 60 or day 90 of a quarter.
Another example is the Huff Model, which is utilized to estimate the potential customer base for a retail location based on factors like store size, product desirability, and competition. It is a type of predictive model that fits the data to help businesses make informed decisions about location and marketing strategies.