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Spurious Correlation Of Ratios
In statistics, spurious correlation of ratios is a form of spurious correlation that arises between ratios of absolute measurements which themselves are uncorrelated. The phenomenon of spurious correlation of ratios is one of the main motives for the field of compositional data analysis, which deals with the analysis of variables that carry only relative information, such as proportions, percentages and parts-per-million. Spurious correlation is distinct from misconceptions about correlation and causality. Illustration of spurious correlation Pearson states a simple example of spurious correlation: The scatter plot above illustrates this example using 500 observations of ''x'', ''y'', and ''z''. Variables ''x'', ''y'' and ''z'' are drawn from normal distributions with means 10, 10, and 30, respectively, and standard deviations 1, 1, and 3 respectively, i.e., : \begin x,y & \sim N(10,1) \\ z & \sim N(30,3) \\ \end Even though ''x'', ''y'', and ''z'' are statistically inde ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spurious Correlation (Colour)
Spurious may refer to: * Spurious relationship in statistics * Spurious emission or spurious tone in radio engineering * Spurious key in cryptography * Spurious interrupt in computing * Spurious wakeup A spurious wakeup happens when a thread wakes up from waiting on a condition variable that's been signaled, only to discover that the condition it was waiting for isn't satisfied. It's called spurious because the thread has seemingly been awakened ... in computing * ''Spurious'', a 2011 novel by Lars Iyer {{disambiguation ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistics
Statistics (from German: '' Statistik'', "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. 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.Dodge, Y. (2006) ''The Oxford Dictionary of Statistical Terms'', Oxford University Press. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An ex ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spurious Correlation
In statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more events or variables are associated but '' not'' causally related, due to either coincidence or the presence of a certain third, unseen factor (referred to as a "common response variable", "confounding factor", or " lurking variable"). Examples An example of a spurious relationship can be found in the time-series literature, where a spurious regression is a regression that provides misleading statistical evidence of a linear relationship between independent non-stationary variables. In fact, the non-stationarity may be due to the presence of a unit root in both variables. In particular, any two nominal economic variables are likely to be correlated with each other, even when neither has a causal effect on the other, because each equals a real variable times the price level, and the common presence of the price level in the two data series imparts correlation ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Compositional Data
In statistics, compositional data are quantitative descriptions of the parts of some whole, conveying relative information. Mathematically, compositional data is represented by points on a simplex. Measurements involving probabilities, proportions, percentages, and ppm can all be thought of as compositional data. Ternary plot Compositional data in three variables can be plotted via ternary plots. The use of a barycentric plot on three variables graphically depicts the ratios of the three variables as positions in an equilateral triangle. Simplicial sample space In general, John Aitchison defined compositional data to be proportions of some whole in 1982. In particular, a compositional data point (or ''composition'' for short) can be represented by a real vector with positive components. The sample space of compositional data is a simplex: :: \mathcal^D=\left\. \ The only information is given by the ratios between components, so the information of a composition is preserved un ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Correlation Does Not Imply Causality
The phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed association or correlation between them. The idea that "correlation implies causation" is an example of a questionable-cause logical fallacy, in which two events occurring together are taken to have established a cause-and-effect relationship. This fallacy is also known by the Latin phrase ''cum hoc ergo propter hoc'' ('with this, therefore because of this'). This differs from the fallacy known as ''post hoc ergo propter hoc'' ("after this, therefore because of this"), in which an event following another is seen as a necessary consequence of the former event, and from conflation, the errant merging of two events, ideas, databases, etc., into one. As with any logical fallacy, identifying that the reasoning behind an argument is flawed does not necessarily imply that the resulting co ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Independence (probability Theory)
Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes. Two events are independent, statistically independent, or stochastically independent if, informally speaking, the occurrence of one does not affect the probability of occurrence of the other or, equivalently, does not affect the odds. Similarly, two random variables are independent if the realization of one does not affect the probability distribution of the other. When dealing with collections of more than two events, two notions of independence need to be distinguished. The events are called pairwise independent if any two events in the collection are independent of each other, while mutual independence (or collective independence) of events means, informally speaking, that each event is independent of any combination of other events in the collection. A similar notion exists for collections of random variables. Mutual independence implies pairwise independe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Coefficient Of Variation
In probability theory and statistics, the coefficient of variation (CV), also known as relative standard deviation (RSD), is a standardized measure of dispersion of a probability distribution or frequency distribution. It is often expressed as a percentage, and is defined as the ratio of the standard deviation \sigma to the mean \mu (or its absolute value, The CV or RSD is widely used in analytical chemistry to express the precision and repeatability of an assay. It is also commonly used in fields such as engineering or physics when doing quality assurance studies and ANOVA gauge R&R, by economists and investors in economic models, and in neuroscience. Definition The coefficient of variation (CV) is defined as the ratio of the standard deviation \ \sigma to the mean \ \mu , c_ = \frac. It shows the extent of variability in relation to the mean of the population. The coefficient of variation should be computed only for data measured on scales that have a meaningfu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Correlation
In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are '' linearly'' related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve. 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. H ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Galton
Sir Francis Galton, Fellow of the Royal Society, FRS Royal Anthropological Institute of Great Britain and Ireland, FRAI (; 16 February 1822 – 17 January 1911), was an English Victorian era polymath: a statistician, sociologist, psychologist, Anthropology, anthropologist, tropical Exploration, explorer, geographer, Invention, inventor, meteorologist, proto-geneticist, Psychometrics, psychometrician and a proponent of social Darwinism, eugenics, and scientific racism. He was knighted in 1909. Galton produced over 340 papers and books. He also created the statistical concept of correlation and widely promoted regression toward the mean. He was the first to apply statistical methods to the study of human differences and inheritance of intelligence, and introduced the use of questionnaires and Statistical survey, surveys for collecting data on human communities, which he needed for genealogical and biographical works and for his anthropometrics, anthropometric studies. He was ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Walter Frank Raphael Weldon
Walter Frank Raphael Weldon FRS (15 March 1860 – 13 April 1906), was an English evolutionary biologist and a founder of biometry. He was the joint founding editor of ''Biometrika'', with Francis Galton and Karl Pearson. Family Weldon was the second child of the journalist and industrial chemist, Walter Weldon, and his wife Anne Cotton. On 13 March 1883, Weldon married Florence Tebb, daughter of the social reformer William Tebb. Life and education Medicine was his intended career and he spent the academic year 1876-1877 at University College London. Among his teachers were the zoologist E. Ray Lankester and the mathematician Olaus Henrici. In the following year he transferred to King's College London and then to St John's College, Cambridge in 1878. There Weldon studied with the developmental morphologist Francis Balfour who influenced him greatly; Weldon gave up his plans for a career in medicine. In 1881 he gained a first-class honours degree in the Natural Science Tr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Normalization (statistics)
In statistics and applications of statistics, normalization can have a range of meanings. In the simplest cases, normalization of ratings means adjusting values measured on different scales to a notionally common scale, often prior to averaging. In more complicated cases, normalization may refer to more sophisticated adjustments where the intention is to bring the entire probability distributions of adjusted values into alignment. In the case of normalization of scores in educational assessment, there may be an intention to align distributions to a normal distribution. A different approach to normalization of probability distributions is quantile normalization, where the quantiles of the different measures are brought into alignment. In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
John Aitchison
John Aitchison (22 July 1926 – 23 December 2016) was a Scottish statistician. Career John Aitchison studied at the Universitiy of Edinburgh after being uncomfortable explaining to his headmaster that he didn’t plan to attend university. He graduated in 1947 with an MA in mathematics. After two years wherein he did actuarial work, he also attended Trinity College, Cambridge. He had a scholarship to do so, and graduated in 1951 with a BA focused on statistics. The year after he graduated, he joined the Department of Applied Economics at Cambridge as a statistician. He continued his work at Cambridge until 1956, when he was offered the position of Lecturer of Statistics at the University of Glasgow. During his time at Glasgow, he wrote ''The lognormal distribution, with special reference to its uses in economics (1957)'' with J A C Brown (who he met at Cambridge). However, he left Glasgow in 1962, when the University of Liverpool offered him the positions of Senior Lec ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
