Analytics Data Science And Artificial Intelligence Systems For Decision Support 11th Global Edition Ramesh Sharda, Dursun Delen, Efraim Turban - Solutions

List and describe the main steps to follow in developing a linear regression model.
What are the commonalities and differences between regression and correlation?
What are the most commonly pronounced assumptions for linear regression? What is crucial to the regression models against these assumptions?
What are the commonalities and differences between linear regression and logistic regression?
What are the main types of charts/graphs? Why are there so many of them?
What is time series? What are the main forecasting techniques for time-series data?
What is a business report? Why is it needed?
What is an information dashboard? What does it present?
Do you think information/performance dashboards are here to stay? Or are they about to be outdated? What do you think will be the next big wave in BI and business analytics in terms of data/information visualization?
Data mining encompasses a variety of methods and processes aimed at making sense of Big Data for research and business purposes. Based on the literature available both in academic journals and on the Web, discuss the differences between reporting, data mining, and online analytical processing and
What are the best practices in designing highly informative dashboards?
When a car or truck owner brings a vehicle into an Acura or Honda dealership in the United States, there's more to the visit than a repair or a service check. During each visit, the service technicians generate data on the repairs, including any warranty claims to American Honda Motor Co., Inc.,
When card issuers first started using automated business rules software to counter debit and credit card fraud, the limits on that technology were quickly evident: Customers reported frustrating payment rejections on dream vacations or critical business trips. Visa works with its clients to improve
With a group of peers, discuss the six phases of CRISP-DM processes by evaluating their comparative challenges and trying, if possible, to provide practical examples of its application.
What are the most popular free data mining tools? Why are they gaining overwhelming popularity?
For this exercise, you will replicate (on a smaller scale) the box-office prediction modeling explained in Application Case 4.6. Download the training data set from Online File W4.2, MovieTrain.xlsx, which is in Microsoft Excel format. Use the data description given in Application Case 4.6 to
What are the most common data mining mistakes/blunders? How can they be alleviated or completely eliminated?
An OLAP Cube is a multidimensional array of data that can be analyzed by OLAP, a computer-based technique for processing data to find insights. For instance, you would analyze company data using variables such as product, time scale, and location to compare expenses and/or sales. Many repositories
What are the key differences between the major data mining tasks?
Influence Health, Inc. provides the healthcare industry's only integrated digital consumer engagement and activation platform. It enables providers, employers, and payers to positively influence consumer decision making and health behaviors well beyond the physical care setting through personalized
In this assignment, you will use a free/open source data mining tool, KNIME (knime.org), to build predictive models for a relatively small Customer Churn Analysis data set. You are to analyze the given data set (about the customer retention/attrition behavior for 1,000 customers) to develop and
Moving beyond the chapter discussion, where else can association be used?
What are the most popular neural network architectures?
What is special about the Naïve Bayes algorithm? What is the meaning of “Naïve” in this algorithm?
What are Bayesian networks? What is special about them?
What is a model ensemble, and where can it be used analytically?
What are the commonalities and differences between biological and artificial neural networks?
What types of problems are solved with Kohonen SOM ANN architecture?
What are the advantages and disadvantages of Naïve Bayes compared to other machine-learning methods?
What is the relationship between Naïve Bayes and Bayesian networks?
What are the different types of model ensembles?
What types of business problems can be solved with ANN?
How does Hopfield ANN architecture work? To what type of problems can it be applied?
What type of data can be used in Naïve Bayes algorithm? What type of predictions can be obtained from it?
What is the process of developing a Bayesian networks model?
Why are ensembles gaining popularity over all other machine-learning trends?
What is the process of developing and testing a Naïve Bayes classifier?
What are the advantages and disadvantages of Bayesian networks compared to other machine-learning methods?
What is the difference between bagging- and boosting-type ensemble models?
What is Tree Augmented Naïve (TAN) Bayes and how does it relate to Bayesian networks?
What are the advantages and disadvantages of ensemble models?
Analytics has been used by many businesses, organizations, and government agencies to learn from past experiences to more effectively and efficiently use their limited resources to achieve their goals and objectives. Despite all the promises of analytics, however, its multidimensional and
Read about the use of AI in defense on KPMG’s Web site (https://kpmg.com/xx/en/home/insights/2020/04/digital-adoption-and-transformation.html). Write a report.
Why do you think there are many different types of charts and graphs?
What is an information dashboard? Why is it so popular?
In the ultra-competitive telecommunications industry, staying relevant to consumers while finding new sources of revenue is critical, especially since current revenue sources are in decline.For Fortune 13 powerhouse Verizon, the secret weapon that catapulted the company into the nation's largest
How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.
Download the “Voting Behavior” data and the brief data description from the book’s Web site. This is a data set manually compiled from counties all around the United States. The data are partially processed, that is, some derived variables have been created.Your task is to thoroughly
How do you describe the importance of data in analytics? Can we think of analytics without data?
What are data? How do data differ from information and knowledge?
Why are the original/raw data not readily usable by analytics tasks?
What is the relationship between statistics and business analytics?
What is regression, and what statistical purpose does it serve?
What are the main categories of data? What types of data can we use for BI and analytics?
What are the main differences between descriptive and inferential statistics?
What are the commonalities and differences between regression and correlation?
A leaky faucet. A malfunctioning dishwasher. A cracked sprinkler head. These are more than just a headache for a home owner or business to fix. They can be costly, unpredictable, and, unfortunately, hard to pinpoint.Through a combination of wireless water meters and a data-analytics-driven,
Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
Go to teradatauniversitynetwork.com. Look for an article that deals with the nature of data, management of data, and/or governance of data as it relates to BI and analytics, and critically analyze the content of the article.
Where do the data for business analytics come from?
Can we use the same data representation for all analytics models? Why, or why not?
What does it mean to clean/scrub the data? What activities are performed in this phase?
List and briefly define the central tendency measures of descriptive statistics.
What is OLS? How does OLS determine the linear regression line?
Find and explain the role of two types of charts that are not covered in this section.
Predicting the outcome of a college football game (or any sports game, for that matter) is an interesting and challenging problem. Therefore, challenge-seeking researchers from both academics and industry have spent a great deal of effort on forecasting the outcome of sporting events. Large amounts
What is the difference between traditional AI and augmented intelligence?
In early 2018, Amazon.com opened its first fully automated convenience store in downtown Seattle. The company had had success with this type of store during 2017, experimenting with only the company's employees. Shoppers enter the store, pick up products, and go home. Their accounts are charged
Go to microstrategy.com. Find information on the five styles of BI. Prepare a summary table for each style.
What is a common thread in the examples discussed in image analytics?
Relate types of AI to cognitive computing.
Go to oracle.com, and click the Hyperion link under Applications. Determine what the company’s major products are. Relate these to the support technologies cited in this chapter.
Can you think of other applications using satellite data along the lines presented in this section?
List five major AI applications for increasing the food supply.
Go to the TUN questions site. Look for BSI videos. Review the video of “Case of Retail Tweeters.” Prepare a one-page summary of the problem, proposed solution, and the reported results. You can also find associated slides on slideshare.net.
List five contributions of AI in medical care.
Review the Analytics Ecosystem section. Identify at least two additional companies in at least five of the industry clusters noted in the discussion.
KONE is a global industrial company (based in Finland) that manufactures mostly elevators and escalators and also services over 1.1 million elevators, escalators, and related equipment in several countries. The company employs over 50,000 people.Over 1 billion people use the elevators and
Why is it difficult to make organizational decisions?
List three of the terms that have been predecessors of analytics.
What are three factors that might be part of a PM for season ticket renewals?
What are the major characteristics of AI?
What are the major benefits of intelligent systems convergences?
This story has been reported in numerous places and has almost become a classic example to explain the need for problem identification. Ackoff (as cited in Larson, 1987) described the problem of managing complaints about slow elevators in a tall hotel tower. After trying many solutions for reducing
Go to. Explore the Sports Analytics page, and summarize at least two applications of analytics in any sport of your choice.
Describe the major steps in the decision-making process.
What was the primary difference between the systems called MIS, DSS, and Executive Information Systems?
What are two techniques that football teams can use to do opponent analysis?
List the major benefits of AI.
Why did analytics initiatives fail at such a high rate in the past?
An Post, the state-owned corporation that manages postal services in the Republic of Ireland, presents an interesting and well-documented case of successful innovation in the public sector, which is often plagued by inefficiency and lackluster performance. Established in 1984, An Post is now one of
You are about to buy a car. Using Simon’s (1977) four phase model, describe your activities at each step in making the decision.
Describe the major external environments that can impact decision making.
Did DSS evolve into BI or vice versa?
What other analytics uses can you envision in sports?
What are the major groups in the ecosystem of AI? List the major contents of each.
What synergy can be created by combining AI and analytics?
Siemens is a German company headquartered in Berlin, Germany. It is one of the world’s largest companies focusing on the areas of electrification, automation, and digitalization. It has an annual revenue of 76 billion euros. The visual analytics group of Siemens is tasked with end-to-end
What is a DW? How can DW technology help enable analytics?
Why would a health insurance company invest in analytics beyond fraud detection? Why is it in its best interest to predict the likelihood of falls by patients?

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Analytics Data Science And Artificial Intelligence Systems For Decision Support 11th Global Edition Ramesh Sharda, Dursun Delen, Efraim Turban - Solutions

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