SOLUTIONS MANUAL
** All Chapters included
** Case Solutions
** Conceptual Problems
** Application Problems
,Table of Contents are given below
Chapter 1. Introduction to Business Analytics
Chapter 2. Descriptive Statistics
Chapter 3. Data Visualization
Chapter 4. Data Wrangling: Data Management and Data Cleaning Strategies
Chapter 5. Probability: An Introduction to Modeling Uncertainty
Chapter 6. Unsupervised Machine Learning
Chapter 7. Statistical Inference
Chapter 8. Linear Regression
Chapter 9. Time Series Analysis and Forecasting
Chapter 10. Supervised Machine Learning: Regression Tasks
Chapter 11. Supervised Machine Learning: Classification Tasks
Chapter 12. Spreadsheet Models
Chapter 13. Monte Carlo Simulation
Chapter 14. Linear Optimization Models
Chapter 15. Integer Linear Optimization Models
Chapter 16. Nonlinear Optimization Models
Chapter 17. Decision Analysis
Chapter 18. Artificial Intelligence
,Solution and Answer Guide
JEFFREY D. CAMM, JAMES J. COCHRAN, MICHAEL J. FRY, JEFFREY W. OHLMANN, BUSINESS
ANALYTICS, 2027, 6TH EDITION, 9798214057361; CASE PROBLEM: CAPITAL STATE UNIVERSITY
GAME-DAY MAGAZINES
TABLE OF CONTENTS
CSU Football Magazines Case Solution ............................................................................... 1
CSU FOOTBALL MAGAZINES CASE SOLUTION
Note that there are many ways to approach this case and that student solution may not
match exactly what is provided here.
Managerial Report
Use the concepts you have learned from Chapters 2, 3, 4, 8, 9, and 13 to write a report that
will help Kris analyze football magazine sales in Years 1 through 9 to determine an order
amount for Year 10. You should address each of the following in your report.
1. There is a considerable amount of data available in the file magazines_csu, but not all
of it may be useful for your purposes here. Are there variables contained in the file
magazines_csu that you would exclude from a forecast model to determine football
magazine sales in Year 10? If so, why? Are there particular observations of football
magazine sales from previous years that you would exclude from your forecasting
model? If so, why?
Solutions:
CSU must make their forecast for the upcoming season in July so that they can place
orders for magazines. Therefore, variables for data that are unknown in July when CSU
must place their order are of little help in creating a forecast for magazine sales. Variables
such as Game Day Weather, Total Game Attendance, and Kickoff Temperature will be
unknowns in July. Thus, we would choose to not include these in a forecasting model.
There are also strong intuitive reasons why some of the variables included here could
describe the same information, and thus, would be highly correlated. For instance, an
Opponent’s Previous Season Number of Wins will be highly correlated with an
Opponent’s Previous Season Number of Losses. We can verify this by calculating the
correlation coefficient for Opponent’s Previous Season Number of Wins and
Opponent’s Previous Season Number of Losses which is –0.97. Similarly, the
correlation coefficient for CSU’s Previous Season Number of Wins and CSU’s Previous
Season Number of Losses is –0.99. Therefore, we would generally only want to include
one of these variables in any forecasting model. (As an alternative, we could combine
the variables by calculating Previous Season Win Percentage.) Other highly correlated
variables are Week in Season and Home Game Number (correlation coefficient = 0.97);
and Kickoff Temperature and Week in Season (correlation coefficient = –0.78). This
latter correlation makes sense when you consider that games later in the football
season are likely in late autumn as temperatures are decreasing.
1
, Based on this analysis, we will delete Game Day Weather, Total Game Attendance,
Kickoff Temperature, Opponent’s Previous Season Number of Losses, CSU’s Previous
Season Number of Losses, and Home Game Number.
There are also several individual observations that we might consider removing. Season 8
Game 1 versus Ohio A&M is different from all other data points because it was the CSU
Throwback Jersey Game. There are no similar observations to this game and no
expectations for such a game in the upcoming season. Therefore, we would have strong
reason to discard this observation.
A box plot of Magazine Sales data is shown below. Here we see that several data
points could be considered outliers. The most extreme is Season 3 Week 1 versus
Lincoln University. This game has Magazine Sales of 6,463. No other game had sales
greater than 5,200. And since this is the only game that was played against Lincoln
University and CSU does not play this team in the upcoming season, we would have
strong reason to remove this data point also.
The other two data points with extremely high magazine sales are Season 2, Week 2
versus University of Ames and Season 1 Week 3 versus Urbana College. CSU plays the
University of Ames each year, including in the upcoming season. Therefore, it would be
inadvisable to throw out all data points where CSU plays University of Ames. Instead,
we could create a new categorical variable to indicate a game versus the University of
Ames as it seems this might affect magazine sales. CSU has only played Urbana
College once and will not play the team in the upcoming season. Therefore, we can
probably remove this data point if we think it is an outlier.
Based on our findings here, we will delete the observations for Season 8 Week 1,
Season 3 Week 1, and Season 1 Week 3. Also, we will add a categorical variable for
games where CSU plays the University of Ames.
2
** All Chapters included
** Case Solutions
** Conceptual Problems
** Application Problems
,Table of Contents are given below
Chapter 1. Introduction to Business Analytics
Chapter 2. Descriptive Statistics
Chapter 3. Data Visualization
Chapter 4. Data Wrangling: Data Management and Data Cleaning Strategies
Chapter 5. Probability: An Introduction to Modeling Uncertainty
Chapter 6. Unsupervised Machine Learning
Chapter 7. Statistical Inference
Chapter 8. Linear Regression
Chapter 9. Time Series Analysis and Forecasting
Chapter 10. Supervised Machine Learning: Regression Tasks
Chapter 11. Supervised Machine Learning: Classification Tasks
Chapter 12. Spreadsheet Models
Chapter 13. Monte Carlo Simulation
Chapter 14. Linear Optimization Models
Chapter 15. Integer Linear Optimization Models
Chapter 16. Nonlinear Optimization Models
Chapter 17. Decision Analysis
Chapter 18. Artificial Intelligence
,Solution and Answer Guide
JEFFREY D. CAMM, JAMES J. COCHRAN, MICHAEL J. FRY, JEFFREY W. OHLMANN, BUSINESS
ANALYTICS, 2027, 6TH EDITION, 9798214057361; CASE PROBLEM: CAPITAL STATE UNIVERSITY
GAME-DAY MAGAZINES
TABLE OF CONTENTS
CSU Football Magazines Case Solution ............................................................................... 1
CSU FOOTBALL MAGAZINES CASE SOLUTION
Note that there are many ways to approach this case and that student solution may not
match exactly what is provided here.
Managerial Report
Use the concepts you have learned from Chapters 2, 3, 4, 8, 9, and 13 to write a report that
will help Kris analyze football magazine sales in Years 1 through 9 to determine an order
amount for Year 10. You should address each of the following in your report.
1. There is a considerable amount of data available in the file magazines_csu, but not all
of it may be useful for your purposes here. Are there variables contained in the file
magazines_csu that you would exclude from a forecast model to determine football
magazine sales in Year 10? If so, why? Are there particular observations of football
magazine sales from previous years that you would exclude from your forecasting
model? If so, why?
Solutions:
CSU must make their forecast for the upcoming season in July so that they can place
orders for magazines. Therefore, variables for data that are unknown in July when CSU
must place their order are of little help in creating a forecast for magazine sales. Variables
such as Game Day Weather, Total Game Attendance, and Kickoff Temperature will be
unknowns in July. Thus, we would choose to not include these in a forecasting model.
There are also strong intuitive reasons why some of the variables included here could
describe the same information, and thus, would be highly correlated. For instance, an
Opponent’s Previous Season Number of Wins will be highly correlated with an
Opponent’s Previous Season Number of Losses. We can verify this by calculating the
correlation coefficient for Opponent’s Previous Season Number of Wins and
Opponent’s Previous Season Number of Losses which is –0.97. Similarly, the
correlation coefficient for CSU’s Previous Season Number of Wins and CSU’s Previous
Season Number of Losses is –0.99. Therefore, we would generally only want to include
one of these variables in any forecasting model. (As an alternative, we could combine
the variables by calculating Previous Season Win Percentage.) Other highly correlated
variables are Week in Season and Home Game Number (correlation coefficient = 0.97);
and Kickoff Temperature and Week in Season (correlation coefficient = –0.78). This
latter correlation makes sense when you consider that games later in the football
season are likely in late autumn as temperatures are decreasing.
1
, Based on this analysis, we will delete Game Day Weather, Total Game Attendance,
Kickoff Temperature, Opponent’s Previous Season Number of Losses, CSU’s Previous
Season Number of Losses, and Home Game Number.
There are also several individual observations that we might consider removing. Season 8
Game 1 versus Ohio A&M is different from all other data points because it was the CSU
Throwback Jersey Game. There are no similar observations to this game and no
expectations for such a game in the upcoming season. Therefore, we would have strong
reason to discard this observation.
A box plot of Magazine Sales data is shown below. Here we see that several data
points could be considered outliers. The most extreme is Season 3 Week 1 versus
Lincoln University. This game has Magazine Sales of 6,463. No other game had sales
greater than 5,200. And since this is the only game that was played against Lincoln
University and CSU does not play this team in the upcoming season, we would have
strong reason to remove this data point also.
The other two data points with extremely high magazine sales are Season 2, Week 2
versus University of Ames and Season 1 Week 3 versus Urbana College. CSU plays the
University of Ames each year, including in the upcoming season. Therefore, it would be
inadvisable to throw out all data points where CSU plays University of Ames. Instead,
we could create a new categorical variable to indicate a game versus the University of
Ames as it seems this might affect magazine sales. CSU has only played Urbana
College once and will not play the team in the upcoming season. Therefore, we can
probably remove this data point if we think it is an outlier.
Based on our findings here, we will delete the observations for Season 8 Week 1,
Season 3 Week 1, and Season 1 Week 3. Also, we will add a categorical variable for
games where CSU plays the University of Ames.
2
