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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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 k-means× 463 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. meta-analysis× 451 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… multicollinearity× 445 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… sample× 432 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. text-mining× 432 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… descriptive-statistics× 430 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… markov-process× 422 A stochastic process with the property that the future is conditionally independent of the past, given the present. spatial× 420 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… matrix× 418 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. aic× 417 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. scikit-learn× 415 Machine learning framework for Python. assumptions× 406 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… dimensionality-reduction× 396 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. sem× 392 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… bias× 390 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]). likelihood× 386 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 $θ: \text{L}(θ)=\text{P}(θ;X=x)$ lme4× 384 an R package to fit linear and generalized linear mixed-effects models. fixed-effects-model× 380 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… normality× 378 Refers to the normal distribution, the Gaussian continuous probability distribution. logit× 376 A name given to the log-odds function, which maps probabilities to the real line. likert× 373 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… r-squared× 368 The coefficient of determination, usually symbolized by $R^2$, is the proportion of the total response variance explained by a regression model. roc× 363 Receiver Operating Characteristic, also known as the ROC curve. power-analysis× 354 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… effect-size× 353 "a measure of the strength of a phenomenon or a sample-based estimate of that quantity" [Wikipedia]. computational-statistics× 353 Refers to the interface of statistics and computing; the use of algorithms and software for statistical purposes. fitting× 352 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. … convergence× 349 Convergence generally means that a sequence of a certain sample quantity approaches a constant as the sample size tends to infinity. summary-statistics× 347 A brief numerical description of a set of data. random-generation× 345 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. ranking× 339 ordering data from highest to lowest or *vice versa.* For questions about *constructing* scores to use in ranking, please use the "valuation" tag, too. negative-binomial× 339 A discrete, univariate distribution modelling the number of ${\rm Bernoulli}(p)$ trial successes until a specified number of failures occur. stationarity× 329 one whose joint distribution is constant over time. A weakly stationary process or series is one whose mean and covariance function (variance and autocorrelati… count-data× 329 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… glmm× 329 Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data). prior× 328 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…