First, the domain of w3cgeo:
Because practitioners of the statistical analysis often address particular applied decision problems, methods developments is consequently motivated by the search to a better decision making under uncertainties.
Decision making process under uncertainty is largely based on application of statistical data analysis for probabilistic risk assessment of your decision. Managers need to understand variation for two key reasons. First, so that they can lead others to apply statistical thinking in day to day activities and secondly, to apply the concept for the purpose of continuous improvement.
This course will provide you with hands-on experience to promote the use of statistical thinking and techniques to apply them to make educated decisions whenever there is variation in business data.
Therefore, it is a course in statistical thinking via a data-oriented approach. Statistical models are currently used in various fields of business and science. However, the terminology differs from field to field. For example, the fitting of models to data, called calibration, history matching, and data assimilation, are all synonymous with parameter estimation.
Your organization database contains a wealth of information, yet the decision technology group members tap a fraction of it. Employees waste time scouring multiple sources for a database.
The decision-makers are frustrated because they cannot get business-critical data exactly when they need it. Therefore, too many decisions are based on guesswork, not facts. Many opportunities are also missed, if they are even noticed at all.
Knowledge is what we know well. Information is the communication of knowledge. In every knowledge exchange, there is a sender and a receiver. The sender make common what is private, does the informing, the communicating.
Information can be classified as explicit and tacit forms. The explicit information can be explained in structured form, while tacit information is inconsistent and fuzzy to explain.
Know that data are only crude information and not knowledge by themselves.
Data is known to be crude information and not knowledge by itself. The sequence from data to knowledge is: Data becomes information, when it becomes relevant to your decision problem. Information becomes fact, when the data can support it.
Facts are what the data reveals. However the decisive instrumental i. Fact becomes knowledge, when it is used in the successful completion of a decision process.
Once you have a massive amount of facts integrated as knowledge, then your mind will be superhuman in the same sense that mankind with writing is superhuman compared to mankind before writing.
The following figure illustrates the statistical thinking process based on data in constructing statistical models for decision making under uncertainties. The above figure depicts the fact that as the exactness of a statistical model increases, the level of improvements in decision-making increases.
That's why we need statistical data analysis. Statistical data analysis arose from the need to place knowledge on a systematic evidence base. This required a study of the laws of probability, the development of measures of data properties and relationships, and so on.
Statistical inference aims at determining whether any statistical significance can be attached that results after due allowance is made for any random variation as a source of error.3. Methodology. In order to find out the requirements for the deliverables of the Working Group, use cases were collected.
For the purpose of the Working Group, a use case is a story that describes challenges with respect to spatial data on the Web for existing or envisaged information systems. 3. Methodology. In order to find out the requirements for the deliverables of the Working Group, use cases were collected.
For the purpose of the Working Group, a use case is a story that describes challenges with respect to spatial data on the Web for existing or envisaged information systems. This document advises on best practices related to the publication of spatial data on the Web; the use of Web technologies as they may be applied to location.
The best practices presented here are intended for practitioners, including Web developers and geospatial experts, and are compiled based on evidence of real-world application. Agricultural Education. AGRI Interdisciplinary Agricultural Science and Technology.
This course is designed to develop competencies of agricultural science teachers to teach essential elements in agricultural business, agricultural mechanization, animal science, and horticulture and crop science. This course is designed for students pursuing accounting or business careers and who are interested in gaining a more thorough knowledge of accounting principles and procedures to analyze financial data.
The purpose of this page is to provide resources in the rapidly growing area of computer-based statistical data analysis. This site provides a web-enhanced course on various topics in statistical data analysis, including SPSS and SAS program listings and introductory routines.
Topics include questionnaire design and survey sampling, .