Author: chris

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.)

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.)

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.)

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.)

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.)

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.)

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.)

The motion picture industry is a competitive business. More than 50 studios produce a total of 300 to 400 new motion pictures each year, and the financial success of each motion picture varies considerably. Gross sales for the opening weekend, the total gross sales, the number of theaters the movie was shown in, and the number of weeks the motion picture was open are common variables used to measure the success of a motion picture. Data collected for a sample of 100 motion pictures produced in 20XX are contained in the file named Movies, linked at the bottom of the page. Use all 100 data points.

Managerial Report

Prepare a report (see below) using the numerical methods of descriptive statistics presented in this module to learn how each of the variables contributes to the success of a motion picture. Be sure to include the following three (3) items in your report.

Descriptive statistics (mean, median, range, and standard deviation) for each of the four variables along with an explanation of what the descriptive statistics tell us about the motion picture industry.
Use the z-score to determine which movies, if any, should be considered high-performance outliers in each of the four variables. If there are any outliers in any category, please list them and state for which category they are an outlier. Describe which method you used to make your determination.
Descriptive statistics (correlation coefficient) showing the relationship between total gross sales and each of the other three variables. Evaluate the relationships between total gross sales and each of the other three variables. Use tables, charts, graphs, or visual dashboards to support your conclusions.

Write a report that adheres to the Written Assignment Requirements under the heading you should have in-text citations and a reference page.

Your report must contain the following:

A title page in APA style.
An introduction that summarizes the problem.
The body of the paper should answer the questions posed in the problem by communicating the results of your analysis. Include results of calculations, as well as charts and graphs, where appropriate.
A conclusion paragraph that addresses your findings and what you have determined from the data and your analysis.

Submit your Excel file in addition to your report.

Advertising and the Hierarchy of Needs

Find three commercials of products in the same industry. For example, you could choose three brands from the automobile industry such as Mercedes, Harley Davidson, and Ford. Each brand should have different target markets. You may use YouTube videos or commercials from the company’s websites. Be sure to properly cite the sources of the videos.

In a media presentation of your choice, assess how each ad draws on the elements in Maslow’s Hierarchy of Needs to appeal to prospective customers. Include in your assessment a comparison of the ads and the approaches they take to addressing these needs. For each ad,Briefly explain the target markets and the value propositions afforded by the respective products. Is there an overarching theme in all three? Include the demographics segmentation for each brand (age, gender, ethnicity, income and wealth, occupation, marital status, household type and size, etc.) What, if any, distinctive characteristics distinguish them from one another?

Advertising and the Hierarchy of Needs

Find three commercials of products in the same industry. For example, you could choose three brands from the automobile industry such as Mercedes, Harley Davidson, and Ford. Each brand should have different target markets. You may use YouTube videos or commercials from the company’s websites. Be sure to properly cite the sources of the videos.

In a media presentation of your choice, assess how each ad draws on the elements in Maslow’s Hierarchy of Needs to appeal to prospective customers. Include in your assessment a comparison of the ads and the approaches they take to addressing these needs. For each ad, briefly explain the target markets and the value propositions afforded by the respective products. Is there an overarching theme in all three? Include the demographics segmentation for each brand (age, gender, ethnicity, income and wealth, occupation, marital status, household type and size, etc.) What, if any, distinctive characteristics distinguish them from one another?

Your presentation should meet the following requirements:

PowerPoint
11-14 slides in length with complete speaker’s notes (not including title and reference slides)
All this assignment has to be formatted according to APA Requirements