Imagine if you owned a small car wash chain and you were considering opening several more locations in different parts of the country. After operating for years locally, you gathered several sets of data that showed how seasonal changes affected the amount of cars being washed, the amount of money customers were willing to spend per wash, and what percentage of customers were willing to upgrade their service to have their cars fully detailed. With all this data to rely on, you feel confident that you will have an advantage over your competitors as you open your new locations. However, after investing in new properties and talent across the country, you find that you were unprepared to meet the labor demands in the Southeast region because of how a different set of seasonal changes affected demand, the customers in the Midwest region were unwilling to pay as much as you expected, and demand for detailing services in the Northwest region was minimal compared to your data set. You had thought that since your data was coming from multiple locations in your area it would be representative of the variations across the country, but you realize how you were wrong in your assumptions. How different would the scenario be if you were able to gather those from the other regions before expanding the company?
In this Discussion, you will examine the importance of applying statistical concepts to data in order to create information that will guide decision making. You will gather data on the costs that cigarettes incur for people who smoke and consider ways in which various professionals can use that information to address smoking from a public health perspective, to increase profits from an economic perspective, or more.
To prepare for this Discussion:
- Review this week’s Learning Resources, focusing in particular on the Descriptive Statistics section with resources on defining and calculating averages, mean, median, and mode.
- Call at least 10 convenience stores and/or grocery stores in your area and ask for the price of a pack of cigarettes. It can be for any brand, but make sure that the prices are all for the same-sized pack of the same brand. As an alternate, you can look up prices online or visit physical locations.
- From those data, calculate the average cost of a pack of cigarettes in your area for the brand you chose.
- Calculate the average cost per year for a one-pack-per-day smoker in your area.
- Review the Academic Writing Expectations for 2000/3000-Level Courses, provided in this week’s Learning Resources.
Note: Be sure to retain the data that you collected. You will use this data again in the Week 3 Discussion.
By Day 3
Post a 150- to 225-word (2- to 3-paragraph) explanation of how the information from calculating the average costs of a product can be used by businesses and professionals. In your explanation, do the following:
- Include your data set and the results from your calculations.
- Explain how at least one of the following might use the information from this analysis: retailers, suppliers, health care professionals, public health professionals, or those from another business or profession.
- Explain why it was important to summarize the prices, instead of just calling one retailer for the price? Would the mean or the median be a more representative number, given your data?
- To support your response, be sure to reference at least one properly cited scholarly source.
By Day 5
Respond with at least 75 words (1 paragraph) each to two or more of your colleagues’ postings by doing at least one of the following:
- Check your peer’s average by calculating their data set and comment if you received a different answer.
- Describe any differences in the data and results between your initial response and theirs: Was the average price the same in their area? What might have led to any differences?
- Explain whether you agree (or disagree) on how the data might be used by businesses or professionals.
- Explain whether you agree (or disagree) on why it was important to summarize, as well as if the mean or median would be a more representative number for your colleague’s data.
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