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Statistics is the study of the collection, organization, analysis, interpretation and presentation of data. It deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments. When analyzing data, it is possible to use one of two statistics methodologies: descriptive statistics or inferential statistics. In applying statistics to e.g. a scientific, industrial, or societal problem, it is necessary to begin with a population or process to be studied. Populations can be diverse topics such as "all persons living in a country" or "every atom composing a crystal". Ideally, statisticians compile data about the entire population (an operation called census). This may be organized by governmental statistical institutes. Descriptive statistics can be used to summarize the population data. Numerical descriptors include mean and standard deviation for continuous data types (like income), while frequency and percentage are more useful in terms of describing categorical data (like race).
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When a census is not feasible, a chosen subset of the population called a sample is studied. Once a sample that is representative of the population is determined, data is collected for the sample members in an observational or experimental setting. Again, descriptive statistics can be used to summarize the sample data. However, the drawing of the sample has been subject to an element of randomness, hence the established numerical descriptors from the sample are also due to uncertainty. In order to still draw meaningful conclusions about the entire population, inferential statistics is needed. It uses patterns in the sample data to draw inferences about the population represented, accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation) and modeling relationships within the data (for example, using regression analysis). Inference can extend to forecasting, prediction and estimation of unobserved values either in or associated with the population being studied; it can include extrapolation and interpolation of time series or spatial data, and can also include data mining.
Relevant conferences, workshops and exhibitions
1. 11th Applied Statistics 2014 International Conference September 21 - 24, 2014 Ribno (Bled), Slovenia
2. The 21st International Conference on Computational Statistics August 19-22, 2014, Geneva
3. IISA 2014 conference July 11-13, 2014 in Riverside, California.
4. Australian statistical conference in conjunction with the Institute of Mathematical Statistics annual meeting July 7-10, 2015, Australian Technology park, Sydney
5. The ERCIM 2014, University of Pisa, Italy, 6-8 December 2014
6. International Statistical Institute Regional Statistics Conference 2014
7. 11th International Vilnius Conference on Probability Theory and Mathematical Statistics
8. International Conference on Robust Statistics 2015 (ICORS 2015) Indian Statistical Institute, Kolkata, India during January 12 - 16, 2015.
9. 13th Islamic Countries Conference on Statistical Sciences (ICCS-13) December 18-21, 2014, Bogor Agricultural University, Bogor, (Indonesia)
Relevant Societies and Associations
1. The Royal Statistical Society
2. American Statistical Association (ASA)
3. The Statistical Society of Australia
4. Statistical Societies – ISCB
5. Hong Kong Statistical Society
6. Belgian Statistical Society
7. Bernoulli Society for Mathematical Statistics and Probability
8. Information society – Eurostat
9. Surveys & statistics - Law Society of NSW
10. Korean Statistical Society
Companies of Statistical Software
1. ADMB
2. Chronux
3. CBEcon
4. GNU Project
5. Fityk
6. Gretl
7. JMulTi
8. LIBSVM
9. MLPACK
10. OpenBUGS Foundation
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This page was last updated on November 25, 2024