Statistics

A Million Random Digits with 100,000 Normal Deviates is a 1955 book by the RAND Corporation. The book of tables was an important 20th century work in the field of statistics and random numbers. It was produced starting in 1947 by an electronic simulation of a roulette wheel attached to a computer, the results of which were then carefully filtered and tested before being used to generate the table. The RAND table was an important breakthrough in delivering random numbers because such a large and carefully prepared table had never before been available. In addition to being available in book form, one could also order the digits on a series of punch cards as well. ...more on Wikipedia about "A Million Random Digits with 100,000 Normal Deviates"

The absolute deviation of an element of a data set is the absolute difference between that element and a given point. Typically the point from which the deviation is measured is the value of either the median or the mean of the data set. ...more on Wikipedia about "Absolute deviation"

Accuracy, in science, engineering, industry and statistics, is the degree of conformity of a measured/calculated quantity to its actual (true) value. Precision (also called reproducibility or repeatability) is the degree to which further measurements or calculations will show the same or similar results. ...more on Wikipedia about "Accuracy and precision"

The Statistician of the Year was instituted in the year 1987 by the Association of Cricket Statisticians and Historians and is sponsored by Hamlyn. ...more on Wikipedia about "ACS Statistician of the Year"

The Akaike information criterion (AIC) (pronounced, approximately, ah-kah-ee-kay), developed by Professor Hirotugu Akaike (赤池 弘次) in 1971 and proposed in 1974, is a statistical model fit measure. It quantifies the relative goodness-of-fit of various previously derived statistical models, given a sample of data. It uses a rigorous framework of information analysis based on the concept of entropy. The driving idea behind the AIC is to examine the complexity of the model together with goodness of its fit to the sample data, and to produce a measure which balances between the two. ...more on Wikipedia about "Akaike information criterion"

Algorithms for calculating variance play a minor role in statistical computing. A key problem in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with large values. ...more on Wikipedia about "Algorithms for calculating variance"

Statistics shows that if you put a large number of random points on a bounded flat surface you can find many alignments of random points. Some people think that this can be used to prove that ley lines exist due to chance alone (as opposed to supernatural or anthropological explanations). ...more on Wikipedia about "Alignments of random points" shortopedia - now!

The Allan variance, named after David W. Allan, is a measurement of accuracy in clocks. It is also known as the two-sample variance. ...more on Wikipedia about "Allan variance"

In statistics, the alternative hypothesis is the hypothesis proposed to explain a statistically significant difference between results, that is if the Null Hypothesis has been rejected. Therefore, in rejecting the Null and accepting the Alternative, one is assuming that a factor or several factors are having an effect on the samples. ...more on Wikipedia about "Alternative hypothesis"

The American Statistical Association (ASA) is a scientific and educational society in the United States with the stated mission to promote excellence in the application of statistical science across the wealth of human endeavor. ...more on Wikipedia about "American Statistical Association"

These are statistical procedures which can be used to for the analysis of categorical data: ...more on Wikipedia about "Analysis of categorical data"

In statistics, analysis of variance (ANOVA) is a collection of statistical models and their associated procedures which compare means by splitting the overall observed variance into different parts. The initial techniques of the analysis of variance were pioneered by the statistician and geneticist Ronald Fisher in the 1920s and 1930s, and is sometimes known as Fisher's ANOVA or Fisher's analysis of variance. ...more on Wikipedia about "Analysis of variance"

Analytics is the branch of logic dealing with analysis. Business analytics is a term used for more sophisticated forms of business data analysis. ...more on Wikipedia about "Analytics"

An ancestral graph is a graph with three types of edges: directed edge, bidirected edge, and undirected edge such that it can be decomposed into three parts: an undirected subgraph, a directed subgraph, and directed edges pointing from the undirected subgraph to the directed subgraph. ...more on Wikipedia about "Ancestral graph"

In statistics, an ancillary statistic is a statistic whose probability distribution does not depend on which of the probability distributions among those being considered is the distribution of the statistical population from which the data were taken. This concept was introduced by the great statistical geneticist Sir Ronald Fisher. ...more on Wikipedia about "Ancillary statistic"

ANCOVA, or analysis of covariance is a test in statistics that is often implemented in computing packages. ANCOVA is a merger of ANOVA and regression for continuous variables. ANCOVA tests whether certain factors have an effect after controlling for quantitative predictors. The inclusion of covariates increases statistical power because it accounts for the variablity. ...more on Wikipedia about "ANCOVA"

The Anderson-Darling test assesses whether known data come from a specified distribution. The formula for the test statistic $A$ to assess if data

APBRmetrics is the analysis of basketball through objective evidence, especially basketball statistics. APBRmetrics is a cousin to the study of baseball statistics, known as Sabermetrics, and similarly takes its name from the acronym APBR, which stands for the Association for Professional Basketball Research. ...more on Wikipedia about "APBRmetrics"

Applied statistics is the use of statistics and statistical theory in real-life situations. ...more on Wikipedia about "Applied statistics"

In mathematics and statistics, the arithmetic mean of a set of numbers is the sum of all the members of the set divided by the number of items in the set (cardinality). (The word set is used perhaps somewhat loosely; for example, the number 3.8 could occur more than once in such a "set".) If one particular number occurs more times than others in the set, it is called a mode. The arithmetic mean is what pupils are taught very early to call the " average." If the set is a statistical population, then we speak of the population mean. If the set is a statistical sample, we call the resulting statistic a sample mean. ...more on Wikipedia about "Arithmetic mean"

In statistics, an association comes from two variables that are related. Many people confuse association with causation. Association does not imply causation. ...more on Wikipedia about "Association (statistics)"

Autocorrelation is a mathematical tool used frequently in signal processing for analysing functions or series of values, such as time domain signals. It is the cross-correlation of a signal with itself. Autocorrelation is useful for finding repeating patterns in a signal, such as determining the presence of a periodic signal which has been buried under noise, or identifying the fundamental frequency of a signal which doesn't actually contain that frequency component, but implies it with many harmonic frequencies. ...more on Wikipedia about "Autocorrelation"

In statistics, given a time series or continuous signal X_t, the autocovariance is simply the covariance of the signal against a time-shifted version of itself. If each state of the series has a mean, E[X_t] = μ_t, then the autocovariance is given by ...more on Wikipedia about "Autocovariance"

an autoregressive conditional heteroskedasticity (ARCH) model considers the variance of the current error term to be a function of the variances of the previous time period's error terms. ARCH relates the error variance to the square of a previous period's error. It is employed commonly in modeling financial time series that exhibit volatility. ...more on Wikipedia about "Autoregressive conditional heteroskedasticity"

In statistics, an autoregressive integrated moving average (ARIMA) model is a generalisation of an autoregressive moving average or (ARMA) model. These models are fitted to time series data either to better understand the data or to predict future points in the series. The model is generally referred to as an ARIMA(p,d,q) model where the p, d and q are integers greater than or equal to zero and refer to the order of the autoregressive, integrated and moving average parts of the model respectively. ...more on Wikipedia about "Autoregressive integrated moving average"

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