Re: Reference: YouTube Statistics
Edelira Longinotti
Ron S. Kenett is Research Professor at University of Turin, Italy, and chairman of the KPA Group, an international firm focused on creating insights through analytics registered in Israel. He is Past President of the Israeli Statistical Association (ISA) and the European Network for Business and Industrial Statistics (ENBIS), a Fellow, and the 2013 Greenfield Medalist of the Royal Statistical Society. His 200 publications and 12 books are on topics in industrial statistics, biostatistics, process control, design of experiments, customer surveys, and quality management.
Geert Molenberghs is Professor of Biostatistics at the University of Hasselt and University of Leuven in Belgium. His has published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and on the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (2001-2004), Co-Editor for Biometrics (2007-2009), and as President of the International Biometric Society (2004-2005). Professor Molenberghs is currently Co-Editor for Biostatistics (2010-) and is the founding director of the Center for Statistics at Hasselt University, as well as the Director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics, I-BioStat, a joint initiative of Hasselt and Leuven Universities. Professor Molenberghs is the editor and author of several books on longitudinal data analysis.
Fabrizio Ruggeri (Co-Editor, Encyclopedia of Statistics in Quality and Reliability) is Research Director at the Italian National Research Council in Milano. Professor Ruggeri is Adjunct Faculty at Queensland University of Technology, International Professor Affiliate at Polytechnic Institute (New York University), Faculty in the Ph.D. program in Mathematics and Statistics at the University of Pavia, Foreign Faculty in the Ph.D. program in Statistics at the University of Valparaiso, and a Member of the Advisory Board of the Ph.D. program in Mathematical Engineering at Polytechnic of Milano. He is Editor-in-Chief of Applied Stochastic Models in Business and Industry (the official ISBIS journal). Professor Ruggeri is co-author of a book on Bayesian analysis of stochastic processes and co-editor of three books on Bayesian robustness and statistics in healthcare.
This module contains a large number of probability distributions,summary and frequency statistics, correlation functions and statisticaltests, masked statistics, kernel density estimation, quasi-Monte Carlofunctionality, and more.
The following functions can reproduce the p-value and confidence intervalresults of most of the functions above, and often produce accurate results in awider variety of conditions. They can also be used to perform hypothesis testsand generate confidence intervals for custom statistics. This flexibility comesat the cost of greater computational requirements and stochastic results.
This page is part of Statistics 4 beginners, a section in Statistics Explained where statistical indicators and concepts are explained in an simple way to make the world of statistics a bit easier both for pupils and students as well as for all those with an interest in statistics.
Statistics are present in a variety of documents, so consider the mode of the document (i.e. website, pdf document, book) and follow the applicable style rules. Follow the links below for examples on citing other commonly found sources of statistics, including data sets and Australian Bureau of Statistics documents.
CDC tracks the fluoridation status of US community water systems and provides detailed biennial reports as an essential health statistics surveillance tool. Information about populations served by these systems is reported to the Water Fluoridation Reporting System (WFRS) by state drinking water programs.
This invaluable dictionary covers all aspects of statistics, including terms used in computing, mathematics, and probability, presented in a clear and practical way. It also provides biographical entries on over 200 key figures in the field, plus coverage of statistical journals and societies. The new edition features expanded coverage of applied statistics.
It is an invaluable dictionary for statistics students and professionals from a wide range of disciplines, including economics, politics, market research, medicine, psychology, pharmaceuticals, and mathematics, and provides a clear introduction to the subject for the general reader.
CloudWatch aggregates data points based on the length of the period that you specify. For example, if you request statistics with a one-hour period, CloudWatch aggregates all data points with time stamps that fall within each one-hour period. Therefore, the number of values aggregated by CloudWatch is larger than the number of data points returned.
CloudWatch needs raw data points to calculate percentile statistics. If you publish data using a statistic set instead, you can only retrieve percentile statistics for this data if one of the following conditions is true:
The dimensions. If the metric contains multiple dimensions, you must include a value for each dimension. CloudWatch treats each unique combination of dimensions as a separate metric. If a specific combination of dimensions was not published, you can't retrieve statistics for it. You must specify the same dimensions that were used when the metrics were created. For an example, see Dimension Combinations in the Amazon CloudWatch User Guide . For more information about specifying dimensions, see Publishing Metrics in the Amazon CloudWatch User Guide .
The metric statistics, other than percentile. For percentile statistics, use ExtendedStatistics . When calling GetMetricStatistics , you must specify either Statistics or ExtendedStatistics , but not both.
The percentile statistics. Specify values between p0.0 and p100. When calling GetMetricStatistics , you must specify either Statistics or ExtendedStatistics , but not both. Percentile statistics are not available for metrics when any of the metric values are negative numbers.
NAICS was developed under the auspices of the Office of Management and Budget (OMB), and adopted in 1997 to replace the Standard Industrial Classification (SIC) system. It was developed jointly by the U.S. Economic Classification Policy Committee (ECPC), Statistics Canada, and Mexico's Instituto Nacional de Estadistica y Geografia, to allow for a high level of comparability in business statistics among the North American countries.
Universities, educational institutions and other users of ABS statistics may have their own guidelines for citing resources. This is a suggested guide, indicating key elements to include when referencing ABS sources.
This is a past version of the SEER Cancer Statistics Review that includes statistics from 1975 through 2015. If you would like to view the most recent version of the CSR, please visit the CSR Home Page.
Statistics (from German: Statistik, orig. "description of a state, a country")[1][2] is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.[3][4][5] In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments.[6]
Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).[7] Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.
Statistics is a mathematical body of science that pertains to the collection, analysis, interpretation or explanation, and presentation of data,[9] or as a branch of mathematics.[10] Some consider statistics to be a distinct mathematical science rather than a branch of mathematics. While many scientific investigations make use of data, statistics is generally concerned with the use of data in the context of uncertainty and decision-making in the face of uncertainty.[11][12]
In applying statistics to a problem, it is common practice to start with a population or process to be studied. Populations can be diverse topics, such as "all people living in a country" or "every atom composing a crystal". Ideally, statisticians compile data about the entire population (an operation called a 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 (like income), while frequency and percentage are more useful in terms of describing categorical data (like education).
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, drawing the sample contains an element of randomness; hence, the numerical descriptors from the sample are also prone to uncertainty. To draw meaningful conclusions about the entire population, inferential statistics are needed. It uses patterns in the sample data to draw inferences about the population represented while 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 the 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, as well as data mining.
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