Answer the following questions.
1. 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.