Explain the difference between descriptive and inferential statistical methods and give an example of how each could help you draw a conclusion in the real world.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
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Explain the difference between descriptive and inferential statistical methods and give an example of how each could help you draw a conclusion in the real world.
2. You would like to determine whether eating before bed influences sleep patterns. List each step you would take to conduct a statistical study on this topic and explain what you would do to complete each step. Then, answer the questions below.
  • What is your hypothesis on this issue?
  • What type of data will you be looking for?
  • What methods would you use to gather information?
  • How would the results of the data influence decisions you might make about eating and sleeping?
3. A company that sells tea and coffee claims that drinking two cups of green tea daily has been shown to increase mood and well-being. This claim is based on surveys asking customers to rate their mood on a scale of 1–10 after days they drink/do not drink different types of tea. Based on this information, answer the following questions:
  • How would we know if this data is valid and reliable?
  • What questions would you ask to find out more about the quality of the data?
  • Why is it important to gather and report valid and reliable data?
4. Identify two examples of real-world problems that you have observed in your personal, academic, or professional life that could benefit from data driven solutions. Explain how you would use data/statistics and the steps you would take to analyze each problem. You may also choose topics below (or examples from the weekly content) to help support your response:
  • Productivity at work.
  • Financial decisions and budgeting.
  • Health and nutrition.
  • Political campaigns.
  • Quality testing in products.
  • Human resource policies.
  • Algorithms for programming/coding.
  • Accounting & financial policies.
  • Crime reduction and trends.
  • Environmental protection / Emergency preparedness.
5. How does analyzing data on these real-world problems aid in problem-solving and drawing conclusions? Be sure to note the value and benefits of data-driven decision-making.
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