EBK ESSENTIALS OF MIS,
13th Edition
ISBN: 8220106778494
Author: LAUDON
Publisher: PEARSON
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Question
Chapter 10, Problem 1IQ
Program Plan Intro
Fantasy sports:
- This sports is one type of online game.
- It is also known as rotisserie or roto.
- In this game, participants collect imaginary or basic teams of actual players of a qualified sport.
- These teams contest depend on the statistical performance of those players in real games.
- Then the given performance is transformed into points that are assembled and calculated based on a roster chosen by a team manager in each fantasy sports.
Expert Solution
Explanation of Solution
Playing of fantasy sports:
Yes. Four times we have played this game.
Explanation of Solution
Dealing with data about fantasy sports:
- Yes. Have worked with the fantasy sports data.
- Participants of fantasy sports understand the importance of correct predictions and player analysis.
- The valuable terms in fantasy sports are developing important statistics, performance indicators and information.
- Eventually, the ability of technology and fantasy members to calculate future player performance has developed in both information and accuracy.
- There is a model to calculate the performance of fantasy player. This model based in the player performance prediction index (PPI).
- o It is consists of estimates of all players’ performance.
- o The calculation of PPIs uses historical individual player performance information as well as game play information in a least-squares regression.
Explanation of Solution
Reasons to working in fantasy sports roles:
- Fantasy sports are games where applicants take on the role of a coach or general manager.
- The coach manager can plan, establish and deliver an appropriate range of sports activities to single person and team
- The sports manager can handle the business matters such as contracts and trade deals, for sports teams and participants.
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Introduction to Computing Group Activity
Can Data Predict Your Grade?
background
As you move through your academic career, you leave an enormous swath of data: which courses
you chose to register for; which ones you looked at but didn't pick; and how you did on each
homework assignment, test, and project. Could this massive amount of data be analyzed to predict
your grade in a course? Could the data suggest which courses you should take next? Should those
predictions be public to your instructor, to you, and to financial aid officers?
research topics to consider
• Course Signals
• Degree Compass, Austin Peay State University
process
1. Divide the class into teams. Each team will select a webbased tool that allows access to
information.
2. Team members should each think of a situation where a person would benefit from the type
of results data mining can bring to a campus and a situation where it might be undesirable.
3. Team members should select the most powerful and best-constructed…
Chapter 10 Solutions
EBK ESSENTIALS OF MIS,
Ch. 10.2 - Prob. 1CQ1Ch. 10.2 - Prob. 2CQ1Ch. 10.2 - Prob. 3CQ1Ch. 10.2 - Prob. 4CQ1Ch. 10.3 - Prob. 1CQ2Ch. 10.3 - Prob. 2CQ2Ch. 10.3 - Prob. 3CQ2Ch. 10.3 - Prob. 4CQ2Ch. 10 - Prob. 1IQCh. 10 - Prob. 2IQ
Ch. 10 - Prob. 3IQCh. 10 - Prob. 4IQCh. 10 - Prob. 5IQCh. 10 - Prob. 6IQCh. 10 - Prob. 7IQCh. 10 - Prob. 1RQCh. 10 - Prob. 2RQCh. 10 - Prob. 3RQCh. 10 - Prob. 4RQCh. 10 - Prob. 5RQCh. 10 - Prob. 6RQCh. 10 - Prob. 7DQCh. 10 - Prob. 8DQCh. 10 - Prob. 9DQCh. 10 - Prob. 10HMPCh. 10 - Prob. 11HMPCh. 10 - Prob. 14CTPCh. 10 - Prob. 15CSQCh. 10 - Prob. 16CSQCh. 10 - Prob. 17CSQCh. 10 - Prob. 18MLMCh. 10 - Prob. 19MLM
Knowledge Booster
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