# Tags

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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 power× 111 Is a property of a hypothesis testing method: the probability of rejecting the null hypothesis given that it is false, i.e. the probability of not making a type II error. The power of a test depends o… discrete-data× 111 Refers to data generated from a distribution that has a countable sample space. Discrete data may be nominal (e.g. the distribution of race in a sample of individuals) or ordinal (e.g. the number of e… epidemiology× 110 the study of the distribution and spread of disease or illness at the population level. mixture× 109 one that is written as a convex combination of other distributions. large-data× 109 'Large data' refers to situations where the number of observations is so large that it necessitates changes in the way the the data analyst thinks about or conducts the analysis. jags× 107 Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS." (http://mcmc-jags.sourcefo… naive-bayes× 107 standardization× 106 Shifting and rescaling data to assure zero mean and unit variance. bias× 106 Bias, in a statistical framework, means that an estimate of a parameter has an expected value that is not equal to the actual parameter value. There is often a tradeoff between bias and variance - low… ranking× 105 Ranking means identifying the ordering from highest to lowest or vice versa; statistical ranks are the values 1, 2, 3, ..., up to the sample size, assigned to values or attributes which have the highe… longitudinal× 104 collected repeatedly on the same subjects. When there is a long series of data, time series analysis may be appropriate. For shorter series, mixed models (a… kolmogorov-smirnov× 103 a test for goodness of fit of data to a distribution. It is often used to test whether a variable is normally distributed. small-sample× 103 Refers to any statistical complication or problem due to having few data. negative-binomial× 102 A discrete, univariate distribution modelling the number of ${\rm Bernoulli}(p)$ trial successes until a specified number of failures occur. group-differences× 100 Group differences broadly refer to statistics which quantify the differences between two or more subpopulations. causal-inference× 100 Causal inference tries to quantify the effect of a change in $X$ on $Y$ whilst holding constant or eliminating all other relevant factors which might influence this relationship. distance-functions× 99 Distance functions refer to functions used for quantifying the notion of distance between members of a set, or between objects. contingency-tables× 98 Tables of counts (occasionally proportions of marginal counts), arranged by (at least) two marginal categories, displaying bivariate or multivariate frequencies. Sometimes called crosstabs. average× 98 Average most often refers to the arithmetic mean, but more generally to measures of central tendency that use most, or all, of the data values. Examples include trimmed mean, Winsorized mean, harmonic… biostatistics× 97 The science of statistics applied to the analysis of biological or medical data. stationarity× 97 one whose joint distribution is constant over time. A weakly stationary process or series is one whose mean and covariance function (variance and autocorrelati… uniform× 96 The uniform distribution describes a random variable that is equally likely to take any value in its sample space. permutation× 95 a rearrangement of values, items or objects, which arise in various statistical contexts. Permutation tests are statistical tests based on all possible rearrangements of data that are… basic-concepts× 95 autoregressive× 95 a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values. gamma-distribution× 95 A non-negative continuous probability distribution indexed by two strictly positive parameters. fixed-effects-model× 94 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… entropy× 94 A mathematical quantity designed to measure the amount of randomness of a random variable. post-hoc× 93 decided upon after the data has been collected, as opposed to "a priori". unbiased-estimator× 93 Refers to an estimator of a population parameter that "hits the true value" on average. That is, a function of the observed data $\hat{\theta}$ is an unbiased estimator of a parameter $\theta$ if $E(\… continuous-data× 92 called continuous if its set of possible values is uncountable, and the chance that it takes any particular value is zero ($\text{P}(X = x) = 0$for every real number$x\$). A … convergence× 91 Convergence generally means that a sequence of a certain sample quantity approaches a constant as the sample size tends to infinity. cox-model× 90 The Cox model refers to a method of survival analysis. Its great advantage is that it does not require specification of the hazard function on time. finance× 90 The science that describes the management, creation and study of money, banking, credit, investments, assets and liabilities. lme× 90 the function for estimating Linear Mixed Effects models in the nlme package for the R project for statistical computing. For general questions about mixed effects models, use the [mixed-effec… glmm× 87 Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data).