Reference no: EM13904980

Draft a response to each of the bulleted questions below. Each question must have its own response and have a minimum of 50 words.

1. How can you use statistics to support decision making in a manufacturing environment?

2. How does statistics convert raw numbers (raw data) into information?

3. Consider tables, graphs, histograms, box plots, which one is more effective at bringing clarity to the information contained in the data?

4. What is an example of a decision made by the airline industry that was based on inferential statistics?

5. How do we know if the data is accurate?

6. Management of a retail chain has been tracking the growth of sales, regressing the company's sales versus the number of outlets. Their data are weekly, spanning the last 65 weeks, since the chain opened its first outlets. What lurking variable might introduce dependence into the errors of the SRM?

7. Supervisors of an assembly line track the output of the plant. One tool that they use is a simple regression of the count of packages shipped each day versus the number of employees who were active on the assembly line during that day, which varies from 35 to about 50. Identify a lurking variable that might violate one of the assumptions of the SRM.

8. Bookstore Physical stores face increasing competition from online rivals. To compete, a campus bookstore collected data on sales of newly released trade books displayed on shelving near the store entrance. These data give weekly sales per week of trade books over the last two years, the number of titles on display, the amount of shelf space (in feet), and the total sales in the store.

a) What patterns are apparent in the time plot of Trade Sales?

b) The store manager built a regression model with Trade Sales as the response, using Week, Total Sales, Shelf Space, and Titles as explanatory variables. What would you recommend to improve this model? (Hint: Some claim that the typical amount of space per book is more important than the quantity of books.)

c) If the manager uses this model to evaluate a new display for trade books in the next few weeks, would you expect the model to give a fair evaluation of the new display? Why? (Hint: Look at the time plot of the residuals.)