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.

Type to find tags:
× 391
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…
× 381
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…
× 376
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.
× 371
Meta-analysis refers to methods focused on contrasting and combining results from different studies, in the hope of identifying patterns among study results, sources of disagreement among those result…
× 362
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…
× 361
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.
× 359
A stochastic process with the property that the future is conditionally independent of the past, given the present.
× 351
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…
× 350
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.
× 344
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…
× 335
a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hi…
× 334
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…
× 333
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…
× 331
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…
× 328
Refers to the normal distribution, the Gaussian continuous probability distribution.
× 325
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.
× 324
A name given to the log-odds function, which maps probabilities to the real line.
× 321
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]).
× 319
The coefficient of determination, usually symbolized by $R^2$, is the proportion of the total response variance explained by a regression model.
× 318
A brief numerical description of a set of data.
× 316
Receiver Operating Characteristic, also known as ROC curve.
× 316
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…
× 312
Machine learning framework for Python.
× 312
Refers to the interface of statistics and computing; the use of algorithms and software for statistical purposes.
× 309
"a measure of the strength of a phenomenon or a sample-based estimate of that quantity" [Wikipedia].
× 308
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.
× 303
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. …
× 298
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…
× 292
an R package to fit linear and generalized linear mixed-effects models.
× 292
A discrete, univariate distribution modelling the number of ${\rm Bernoulli}(p)$ trial successes until a specified number of failures occur.
× 291
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…
× 286
ordering data from highest to lowest or *vice versa.* For questions about *constructing* scores to use in ranking, please use the "valuation" tag, too.
× 284
Convergence generally means that a sequence of a certain sample quantity approaches a constant as the sample size tends to infinity.
× 284
A non-negative continuous probability distribution indexed by two strictly positive parameters.
× 280
really a special case of multiple linear regression, used in ANOVA-like settings with some continuous covariates in addition to the categorical ones.
× 276
Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data).