Reference no: EM133186550

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.