Glossari
Aquest glossari és una mostra dels termes més representatius de Karl Pearson. Les definicions han estat extretes dels recursos següents:
Vikipedia en català (VIKIPEDIA-CA)
Wikipedia en anglès (WIKIPEDIA-EN)
Wikipedia en castellà (WIKIPEDIA-ES)
Wolfram Science (WOLFRAM)
Biometrika
(Karl Pearson fou cofundador amb Francis Galton i Raphael Weldon)
Biometrika is a peer-reviewed scientific journal published by Oxford University Press for the Biometrika Trust. It was established in October, 1901. The editor-in-chief is A. C. Davison. The principal focus of this journal is theoretical statistics. It was established in 1901 and originally appeared quarterly. It changed to three issues per year in 1977 but returned to quarterly publication in 1992.
Citació: " Biometrika" A: Wikipedia. Wikimedia Foundation, 2011. [en línia]. [Consulta: 22 setembre 2016]. Disponible a: <https://en.wikipedia.org/wiki/Biometrika>
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Biostatistics
Biostatistics is the application of statistics to a wide range of topics in biology. The science of biostatistics encompasses the design of biological experiments, especially in medicine, pharmacy, agriculture and fishery; the collection, summarization, and analysis of data from those experiments; and the interpretation of, and inference from, the results. A major branch of this is medical biostatistics, which is exclusively concerned with medicine and health.
Citació: "Biostatistics" A: Wikipedia. Wikimedia Foundation, 2011. [en línia]. [Consulta: 22 setembre 2016]. Disponible a: <https://en.wikipedia.org/wiki/Biostatistics>
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Contingency table
In statistics, a contingency table is a type of table in a matrix format that displays the (multivariate) frequency distribution of the variables. They are heavily used in survey research, business intelligence, engineering and scientific research. They provide a basic picture of the interrelation between two variables and can help find interactions between them. The term contingency table was first used by Karl Pearson in "On the Theory of Contingency and Its Relation to Association and Normal Correlation",[1] part of the Drapers' Company Research Memoirs Biometric Series I published in 1904.
A crucial problem of multivariate statistics is finding (direct-)dependence structure underlying the variables contained in high-dimensional contingency tables. If some of the conditional independences are revealed, then even the storage of the data can be done in a smarter way (see Lauritzen (2002)). In order to do this one can use information theory concepts, which gain the information only from the distribution of probability, which can be expressed easily from the contingency table by the relative frequencies.
Citació: "Contingency table" A: Wikipedia. Wikimedia Foundation, 2011. [en línia]. [Consulta: 22 setembre 2016]. Disponible a: <https://en.wikipedia.org/wiki/Contingency_table>
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Correlation (Statistics)
In statistics, dependence or association is any statistical relationship, whether causal or not, between two random variables or two sets of data. Correlation is any of a broad class of statistical relationships involving dependence, though in common usage it most often refers to the extent to which two variables have a linear relationship with each other. Familiar examples of dependent phenomena include the correlation between the physical statures of parents and their offspring, and the correlation between the demand for a product and its price.
Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling; however, correlation is not sufficient to demonstrate the presence of such a causal relationship (i.e., correlation does not imply causation).
Citació: "Correlation and dependence" A: Wikipedia. Wikimedia Foundation, 2011. [en línia]. [Consulta: 22 setembre 2016]. Disponible a:
<https://en.wikipedia.org/wiki/Correlation_and_dependence>
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History of statistics
The History of statistics can be said to start around 1749 although, over time, there have been changes to the interpretation of the word statistics. In early times, the meaning was restricted to information about states. This was later extended to include all collections of information of all types, and later still it was extended to include the analysis and interpretation of such data. In modern terms, "statistics" means both sets of collected information, as in national accounts and temperature records, and analytical work which requires statistical inference.
Citació: "History of statistics" A: Wikipedia. Wikimedia Foundation, 2011. [en línia]. [Consulta: 22 setembre 2016]. Disponible a: <https://en.wikipedia.org/wiki/History_of_statistics>
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Mathematical statistics
Mathematical statistics is the application of mathematics to statistics, which was originally conceived as the science of the state — the collection and analysis of facts about a country: its economy, land, military, population, and so forth. Mathematical techniques which are used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure-theoretic probability theory.
Citació: "Mathematical statistics" A: Wikipedia. Wikimedia Foundation, 2011. [en línia]. [Consulta: 22 setembre 2016]. Disponible a: <https://en.wikipedia.org/wiki/Mathematical_statistics>
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Multivariate analysis
Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest.
Citació: "Multivariate analysis" A: Wikipedia. Wikimedia Foundation, 2011. [en línia]. [Consulta: 22 setembre 2016]. Disponible a: <https://en.wikipedia.org/wiki/Multivariate_analysis>
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Pearsons's chi-squared test
Pearson's chi-squared test (χ2) is a statistical test applied to sets of categorical data to evaluate how likely it is that any observed difference between the sets arose by chance. It is suitable for unpaired data from large samples.[1] It is the most widely used of many chi-squared tests (e.g., Yates, likelihood ratio, portmanteau test in time series, etc.) – statistical procedures whose results are evaluated by reference to the chi-squared distribution. Its properties were first investigated by Karl Pearson in 1900.[2] In contexts where it is important to improve a distinction between the test statistic and its distribution, names similar to Pearson χ-squared test or statistic are used.
It tests a null hypothesis stating that the frequency distribution of certain events observed in a sample is consistent with a particular theoretical distribution. The events considered must be mutually exclusive and have total probability 1. A common case for this is where the events each cover an outcome of a categorical variable. A simple example is the hypothesis that an ordinary six-sided die is "fair" (i. e., all six outcomes are equally likely to occur).
Citació: "Pearson's chi-squared test" A: Wikipedia. Wikimedia Foundation, 2011. [en línia]. [Consulta: 22 setembre 2016]. Disponible a: <https://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test>
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Pearson distribution
The Pearson distribution is a family of continuous probability distributions. It was first published by Karl Pearson in 1895 and subsequently extended by him in 1901 and 1916 in a series of articles on biostatistics.
The Pearson system was originally devised in an effort to model visibly skewed observations. It was well known at the time how to adjust a theoretical model to fit the first two cumulants or moments of observed data: Any probability distribution can be extended straightforwardly to form a location-scale family. Except in pathological cases, a location-scale family can be made to fit the observed mean (first cumulant) and variance (second cumulant) arbitrarily well. However, it was not known how to construct probability distributions in which the skewness (standardized third cumulant) and kurtosis (standardized fourth cumulant) could be adjusted equally freely. This need became apparent when trying to fit known theoretical models to observed data that exhibited skewness. Pearson's examples include survival data, which are usually asymmetric.
Citació: "Pearson distribution" A: Wikipedia. Wikimedia Foundation, 2011. [en línia]. [Consulta: 22 setembre 2016]. Disponible a: <https://en.wikipedia.org/wiki/Pearson_distribution>
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Statistical inference
Statistical inference is the process of deducing properties of an underlying distribution by analysis of data.[1] Inferential statistical analysis infers properties about a population: this includes testing hypotheses and deriving estimates. The population is assumed to be larger than the observed data set; in other words, the observed data is assumed to be sampled from a larger population.
Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and does not assume that the data came from a larger population.
Citació: "Statistical inference" A: Wikipedia. Wikimedia Foundation, 2011. [en línia]. [Consulta: 22 setembre 2016]. Disponible a: <https://en.wikipedia.org/wiki/Statistical_inference>
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