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Data Resource Management
Data Resource Management

... Does there seem to be any relationship between companies that look at their data as an asset and companies that are highly successful in their respective industries? ...
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What is GIS

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... variables. Neural networks build their own models with the help of learning process whether the relationships among variables are linear or not. ...
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... increasingly important tool to transform the data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery. ...
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Introduction to Linear Mixed Models

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Chapter 8 Linear regression

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A SAS Macro Solution for Scoring Test Data with Output

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A Future Scenario of interconnected EO Platforms

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Literature Review of Issues in Data Warehousing and OLTP, OLAP

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Using Rapid Prototyping to Develop a Data Mart

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hot-aisle / cold-aisle containment: putting a lid on rising

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Data Modeling [Comparison of data modeling techniques ]

... UML is an object modeling technique  It models object classes instead of entities  In the object oriented world the relationships are called as associations  Cardinality and optionality in UML is conveyed by characters or numbers  Express in the form of more complex upper and lower limits  UML ...
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... • A decision support database that is maintained separately from the organization’s operational database • Support information processing by providing a solid platform of consolidated, historical data for analysis. ...
< 1 ... 23 24 25 26 27 28 29 30 31 ... 76 >

Forecasting

Forecasting is the process of making predictions of the future based on past and present data and analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology, the terms ""forecast"" and ""forecasting"" are sometimes reserved for estimates of values at certain specific future times, while the term ""prediction"" is used for more general estimates, such as the number of times floods will occur over a long period.Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible.
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