EDM involves the uses from different educational backgrounds having their own vision and art to makes use of data mining methods in an effective way.
For the success of EDM, it is very important to have a wide range of meaningful data warehouse so the decision makers can use that data to take right and effective decision but it is very difficult to get meaningful data.
Search our thousands of essays: In a same way, an organization or any university may uses data mining to determine that how the results of students can be improved along with taking care to reduce student retentions. The data mining items I would need to conduct a regression analysis are varied.
For example, I would need variables relating to debt levels on and individual basis. If EDM user is a teacher or any instructor then they must use data mining to detect their students learning performance and other skills so they can analyze that which student need more attention.
Due to these occurrences, regressions must take into all the variables, no matter how farfetched or rare they may be. As a result, a loan will be much higher in New York. Although many problems can persist with linear regression, I believe it provides the best means of explaining the overall relationship between loans and default risk.
Wal-Mart unlike many other retailers…… [Read More] I would use linear regression as it allows a practitioner to see clusters of date scattered around a particular area. There is a difference between EDM and academic analysis which lies in that EDM is a term that access only the educational data whereas academic analysis focus on educational institution effectiveness as well as makes strategy to reduce student retention.
The bank must be sure that the collateral backing the loan is appropriately priced given the market conditions that are prevailing and will prevail in the future.
By omitting these risks from the regression analysis, the outputs were in error. Variable that depend on the proceeding variable can cause problems and errors in the overall regression analysis. Most of the research in the field of EDM focuses on quantitative analysis this is necessary because data mining make use of many techniques based upon quantities like machine learning, artificial intelligence.
Essay UK - http: There are UK writers just like me on hand, waiting to help you. EDM can also get idea from organizational data mining.
About this resource This Information Technology essay was submitted to us by a student in order to help you with your studies. With the higher loan amount, the possibility of default and capital loss is also higher.
I would also need income, education, and demographic information. Inregression analysis failed at financial institutions because they failed to see or account for "tail risk. Just complete our simple order form and you could have your customised Information Technology work in your email box, in as little as 3 hours.
With a higher default risk, the bank will demand higher collateral, more money down, etc. Each of us is qualified to a high level in our area of expertise, and we can write you a fully researched, fully referenced complete original answer to your essay question.
In particularly, a wave of massive loan defaults occurred that nearly crippled the United States financial system. E-learning and learning management system is an another important research area where online instructions are provided by E-learning and communication, collaboration, administration and reporting tools are provided by learning management system.
Many researchers all over the world are making their efforts in the field of EDM ; some of them are as follows- 1. The practitioner must first eliminate non-stationary variables in addition to co dependence. As stated by Campbell and Oblinger in , EDM is a combination of data mining and statistical techniques that assist faculty and advisor to detect the at-risk student so necessary remedial action can be taken to work accordingly.
Refresh or introduce a product at least once every two years. Regressions are also flexible allowing for multiple variables to be used in an explanatory fashion.
The main objective of organizational data mining is to support various organizations to enhance their competitive advantages.
Regressions, through the use of the R squared metric can help an analyst better determine what percentage of the loan defaults can be explained by variables such as income, debt, or other variables. However, solutions such as use of the adjusted R squared metric, the Dickey-Fuller test and others can help eliminate these concerns.
For example, homes in the New York will be more expensive than homes in North Dakota. Provide an open environment for idea generation and brainstorming ndustry leading innovation Update product upgrade cycle. Offline education focuses on face to face contact based knowledge and skills.
Instead of accessing the individual course, this type of analysis focuses on various activities occurring in universities, due to this, this kind of analysis is considered as academic analysis.Overview: Efficient asset allocation through statistical learning methods and comparison of methods for the creation of an index tracking ETF (Exchange traded fund) Datasets: The datasets are chosen from the website of the book “Statistics and Data Analysis for Financial Engineering” by David Ruppert.
Data mining is an analytic process of exploring huge amount of data, extract useful information, finding consistent patterns and trends between variables, and build predictive computer models from the relationship discovered using a combination of classical statistics, machine learning and artificial intelligence.
To the extent a system of record in a data mining application is stable and scalable is the extent to which a data mining application will be able to deliver the critical relationship data, predictive analytics and accurately reflect the associations most critical to companies (Kuhn, Ducasse, Girba, ).
Data mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data.
Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Different researchers have defined the EDM in different ways. As stated by Campbell and Oblinger in , EDM is a combination of data mining and statistical techniques that assist faculty and advisor to detect the at-risk student so necessary remedial action can be taken to work accordingly.
Roots of data mining are mainly on statistics, artificial intelligence, and machine learning techniques. Data mining is built mainly on statistics, e.g, standard distribution, regression analysis, standard variance], standard deviation, cluster analysis etc.
which helps to study data and data relationships.Download