Statistics Statistics In statistics, autoregressive moving average (ARMA) models, sometimes called Box-Jenkins models after George Box and G. M. Jenkins, are typically applied to time series data. ...more on Wikipedia about "Autoregressive moving average model"
In mathematics, there are numerous methods for calculating the average or central tendency of a list of n numbers. The most common method, and the one generally referred to simply as the average, is the arithmetic mean. Please see the table of mathematical symbols for explanations of the symbols used. ...more on Wikipedia about "Average"
In statistics, the use of Bayes factors is a Bayesian alternative to classical hypothesis testing. ...more on Wikipedia about "Bayes factor"
Bayesian inference is a statistical inference in which probabilities are interpreted not as frequencies or proportions or the like, but rather as degrees of belief. The name comes from the frequent use of Bayes' theorem in this discipline. ...more on Wikipedia about "Bayesian inference"
Bayesian search theory is the application of Bayesian statistics to the search for lost objects. It has been used several times to find lost sea vessels, for example the USS Scorpion. The usual procedure is as follows: ...more on Wikipedia about "Bayesian search theory"
In statistics, the Behrens-Fisher problem is the problem of interval estimation and hypothesis testing concerning the difference between the means of two normally distributed populations when the variances of the two populations are not assumed to be equal, based on two independent samples. ...more on Wikipedia about "Behrens-Fisher problem"
Benford's law, also called the first-digit law, states that in lists of numbers from many real-life sources of data, the leading digit 1 occurs much more often than the others (namely about 30% of the time). Furthermore, the larger the digit, the less likely it is to occur as the leading digit of a number. This applies to figures related to the natural world or of social significance; be it numbers taken from electricity bills, newspaper articles, street addresses, stock prices, population numbers, death rates, areas or lengths of rivers, physical and mathematical constants, and processes described by power laws (which are very common in nature). ...more on Wikipedia about "Benford's law"
The central limit theorem in probability theory and statistics states that under certain circumstances the sample mean, considered as a random quantity, becomes more normally distributed as the sample size is increased. The Berry–Esséen theorem, also known as the Berry–Esséen inequality, attempts to quantify the rate at which this convergence to normality takes place. ...more on Wikipedia about "Berry–Esséen theorem"
The Bhattacharya coefficient is an approximate measurement of the amount of overlap between two statistical samples. The coefficient can be used to determine the relative closeness of the two samples being considered. ...more on Wikipedia about "Bhattacharya coefficient"
In statistics, the term bias is used for two different concepts. A biased sample is a statistical sample in which members of the statistical population are not equally likely to be chosen. A biased estimator is one that for some reason on average over- or underestimates the quantity that is being estimated. ...more on Wikipedia about "Bias (statistics)"
In statistics, a bimodal distribution is a distribution with two different peaks — that is, there are two distinct values that measurements tend to center around. Unlike other distributions such as the normal distribution, there is no precise definition of a bimodal distribution. A good example is the height of a person; the heights of males form a roughly normal distribution, as do those of females, but when added together we obtain a bimodal distribution with values clustering around both the averages. ...more on Wikipedia about "Bimodal distribution"
Binary classification is the task of classifying the members of a given set of objects into two groups on the basis of whether they have some property or not. Some typical binary classification tasks are ...more on Wikipedia about "Binary classification"
Biostatistics or biometry is the application of statistics to a wide range of topics in biology. It has particular applications to medicine and to agriculture. ...more on Wikipedia about "Biostatistics"
Blind signal separation, a.k.a. blind source separation, is the separation of a set of signals from a set of mixed signals, without the aid of information (or with very little information) about the nature of the signals. ...more on Wikipedia about "Blind signal separation"
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In the statistical theory of the design of experiments, blocking is the arranging of experimental units in groups (blocks) which are similar to one another. For example, an experiment is designed to test a new drug on patients. There are two levels of the treatment, drug, and placebo, administered to male and female patients in a double blind trial. The sex of the patient is a blocking factor accounting for treatment variablility between males and females. ...more on Wikipedia about "Blocking (statistics)"
In descriptive statistics, a box plot (also known as a box-and-whisker diagram) is a convenient way of graphically depicting the five-number summary, which consists of the smallest observation, lower quartile, median, upper quartile and largest observation. ...more on Wikipedia about "Box plot"
In statistics, the Box-Cox transformation of the variable Y given the "Box-Cox parameter" λ ≥ 0 is defined as ...more on Wikipedia about "Box-Cox transformation"
In econometrics, the Box-Jenkins methodology, named after the statisticians George Box and Gwilym Jenkins, applies autoregressive integrated moving average ARIMA models to find the best fit of a time series to past values of this time series, in order to make forecasts. ...more on Wikipedia about "Box-Jenkins"
it is convenient to transform the multiplicity distribution to the bunching parameters: ...more on Wikipedia about "Bunching parameter"
Calibration in statistics is a reverse process to regression. The calibration problem is the use of known data on the observed relationship between a dependent variable and an independent variable to make estimates of other values of the independent variable from new observations of the dependent variable. ...more on Wikipedia about "Calibration (statistics)"
In statistics, canonical correlation analysis, introduced by Harold Hotelling, is a way of making sense of cross-covariance matrices. ...more on Wikipedia about "Canonical correlation"
In statistics, central tendency is an average of a set of measurements, the word average being variously construed as mean, median, or other measure of location, depending on the context. Central tendency is a descriptive statistic analogous to center of mass in physical terms. The term is used in some fields of empirical research to refer to what statisticians sometimes call "location". A "measure of central tendency" is either a location parameter or a statistic used to estimate a location parameter. ...more on Wikipedia about "Central tendency"
Chauvenet's Criterion is a means of assessing whether one piece of experimental data — an outlier — from a set of observations, is spurious. ...more on Wikipedia about "Chauvenet's criterion"
* Posterior probability density function. This method assumes that the number of toss is fixed and is not under the direct control of the experimenter. The method requires the pdf of the true value of obtaining a particular side be derived and the probability of a "fair coin" be obtained by integrating the pdf by the relevant interval. Based on result, a decision can then be made whether to proclaim the coin as a fair coin. ...more on Wikipedia about "Checking if a coin is fair"
Circular statistics is the subdiscipline of statistics that deals with circular data. ...more on Wikipedia about "Circular statistics"
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