A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.

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a family of cluster analysis methods in which you specify the number of clusters you expect. This is as opposed to hierarchical cluster analysis methods.
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Meta-analysis refers to methods focused on contrasting and combining results from different studies, in the hope of increasing precision and external validity, as well as identifying patterns among st…
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Multicollinearity means predictor variables are correlated with each other, making it harder to determine the role each of the correlated variables is playing. Mathematically, it means the standard er…
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Refers to a subset of data mining concerned with extracting information from data in the form of text by recognizing patterns. The goal of text mining is often to classify a given document into one of…
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a subset of a population. Statistics, in general, is concerned with using samples to make inference about the parameters governing a larger (possibly infinite) population.
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The field of study concerning statistical methods that use space and spatial relationships (such as distance, area, volume, length, height, orientation, centrality and/or other spatial characteristics…
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A stochastic process with the property that the future is conditionally independent of the past, given the present.
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a rectangular array of numbers, symbols, or expressions arranged in rows and columns. The individual items in a matrix are called its elements or entries.
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Descriptive statistics summarize features of a sample, such as mean and standard deviations, median and quartiles, the maximum and minimum. With multiple variables, may include correlations and crosst…
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Refers to the conditions under which a statistics procedure yields valid estimates and/or inference. E.g., many statistical techniques require the assumption that the data are randomly sampled in some…
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AIC stands for the Akaike Information Criterion, which is one technique used to select the best model from a class of models using a penalized likelihood. A smaller AIC implies a better model.
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The difference between the expected value of a parameter estimator & the true value of the parameter. Do NOT use this tag to refer to the [bias-term] / [bias-node] (ie the [intercept]).
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a multivariate technique popular in social sciences. It is based on formulating a set of linear relations between variables, some of which may be latent, and estimating…
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Given a random variable $X$ which arise from a parameterized distribution $F(X;θ)$, the likelihood is defined as the probability of observed data as a function of $θ: \text{L}(θ)=\text{P}(θ;X=x)$
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Refers to techniques for reducing a large number of variables to a smaller number while preserving as much information as possible. Prominent methods include PCA, MDS, Isomap, etc.
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In biostatistics, fixed-effects may mean population-average effects. In econometrics, fixed-effects may represent the observed quantities in terms of explanatory variables that are treated as if the q…
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Machine learning framework for Python.
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Classically, a Likert scale was composed of the sum of many Likert items (ordinal ratings of the amount of agreement with a statement), where all the items were equally valid. Today the term sometimes…
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Refers to the normal distribution, the Gaussian continuous probability distribution.
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A name given to the log-odds function, which maps probabilities to the real line.
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an R package to fit linear and generalized linear mixed-effects models.
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The coefficient of determination, usually symbolized by $R^2$, is the proportion of the total response variance explained by a regression model.
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Receiver Operating Characteristic, also known as ROC curve.
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A brief numerical description of a set of data.
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An inquiry into the quality of a statistical test by calculating the power - the probability of rejecting the null hypothesis given that it is false - under certain circumstances. Power analysis is of…
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The process of fiting some statistical model to a particular set of data. Mostly done on a computer, and using varied numerical methods such as optimization or numerical integration, or simulation. …
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"a measure of the strength of a phenomenon or a sample-based estimate of that quantity" [Wikipedia].
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Refers to the interface of statistics and computing; the use of algorithms and software for statistical purposes.
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The act of generating a sequence of numbers or symbols randomly, or (more often) pseudo-randomly; i.e., with lack of any predictability or pattern.
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Convergence generally means that a sequence of a certain sample quantity approaches a constant as the sample size tends to infinity.
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A discrete, univariate distribution modelling the number of ${\rm Bernoulli}(p)$ trial successes until a specified number of failures occur.
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In Bayesian statistics a prior distribution formalizes information or knowledge (often subjective), available before a sample is seen, in the form of a probability distribution. A distribution with la…
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one whose joint distribution is constant over time. A weakly stationary process or series is one whose mean and covariance function (variance and autocorrelati…
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ordering data from highest to lowest or *vice versa.* For questions about *constructing* scores to use in ranking, please use the "valuation" tag, too.
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non-negative integers representing whole amounts. When such data are the dependent variable in a regression, Poisson or negative binomial regression may be appropriate methods. One comm…
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A non-negative continuous probability distribution indexed by two strictly positive parameters.