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Data Analytics

Many business activities generate data that can be thought of as random. For example, a service manager at an auto shop needs to understand the data for cars coming in for services like oil changes. A variable of interest is the amount of time necessary to service the car, since service time will vary with each car. They can often capture the most relevant characteristics with a simple probability distribution model. The service manager can then analyze the model to make predictions and drive decisions, such as how many technicians to schedule to service demand on a Saturday afternoon.

Respond to the following:

How would you differentiate a discrete from a continuous random variable? Provide a specific example to illustrate the difference.
Provide a scenario when you use might use one type of random sampling method in your industry. Explain why you would choose this method in this scenario, even if another random sampling method could be used?

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