7-2 Report: Interpret T-Tests Results

Overview

You are a business analyst for an organization that distributes pizza ingredients to various restaurants. These ingredients include dough, sauces, and toppings. Your organization wants to expand its customer base but does not know what kind of restaurant or which U.S. region to target. You have been given a Clean Customer Sales Data Set that is a representative sample of the sales at your customers’ restaurants for 2015 and 2016. This also contains household income data for the restaurant zip codes.

Your organization’s leadership is concerned about rising delivery costs and is considering whether they should specifically target pizzerias as customers. They are also considering whether they should only offer morning deliveries. Currently, the organization’s roster of customers includes a wide range of restaurants, many of which are open for business before noon. You have been asked to analyze your customer data set and provide insight and recommendations. First, you must determine differences in the orders placed at different times of the day and compare orders placed in pizzerias with those in other restaurants. You will then need to analyze these differences to determine whether these differences are due to chance or are statistically significant and determine how these may impact your recommendations.

For this activity, you will use MS Excel to perform t-tests on the given data set to determine whether the differences that you see between two groups are significant or due to chance.

Prompt

Create a report that presents the results of the t-tests on the data included in your data set to inform leadership’s decision making. Include the relevant screenshots in the report.

Specifically, you must address the following rubric criteria:

  • Perform T-Tests: Perform t-tests on identified binary (dichotomous) variables to determine differences between data groups.
    • Perform a t-test to investigate customers’ orders based on time of day. Group the orders according to those placed in the morning and those placed after noon.
      • Differentiate between the two groups of orders in terms of:
        • The average number of orders (or volume)
        • The average total amount
    • Perform a t-test by creating a new variable to group the restaurants based on whether or not they are categorized as “Pizza Place.”
      • Determine if there is a difference in the volume and the average order amount between restaurants categorized as Pizza Place and those that are not.
  • Interpret Statistical Results: Determine the statistical significance of your t-test results using the p values generated.
    • Determine whether there’s a statistically significant difference at the p < .05 level.
      • Differentiate between the volume of orders placed in the morning and in the afternoon.
    • Interpret the impact of limiting deliveries to only mornings and the impact on restaurants with significant pre-noon sales.
    • Recommend the time for deliveries if the level of significance was relaxed to p < .10.
    • Interpret the differences between pizzerias and other restaurants and how they influence your recommendation to leadership.
  • Summarize T-Test Results : Summarize the t-test results for a non-technical audience.
    • Explain why you performed a t-test instead of any other statistical test to answer leadership’s questions.
    • Explain your findings in terms of the p < .05 level and how confident you can be in any conclusions drawn from your analysis.
  
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