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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Machine learning framework for Python.
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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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Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data).
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said to have a high reliability if it produces similar results under consistent conditions. DO NOT confuse reliability with validity (see tag wiki).
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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…
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a simple probabilistic classifier based on applying Bayes' theorem with strong independence assumptions. A more descriptive term for the underlying probability model would …
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Group differences broadly refer to statistics which quantify the differences between two or more subpopulations.
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Kernel trick refers to kernel methods in machine learning, such as kernel support vector machine (SVM) or kernel principal components analysis (PCA). It allows to generalize linear techniques to non-l…
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decided upon after the data has been collected, as opposed to "a priori".
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an R package to fit linear and generalized linear mixed-effects models.
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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 …
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Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.
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The quantiles of a distribution refer to points on its cumulative distribution function. Some common quantiles are quartiles and percentiles.
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a test for goodness of fit of data to a distribution. It is often used to test whether a variable is normally distributed.
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'Large data' refers to situations where the number of observations (data points) is so large that it necessitates changes in the way the data analyst thinks about or conducts the analysis. (Not to be …
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The science of statistics applied to the analysis of biological or medical data.
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Gaussian processes refer to stochastic processes whose realization consists of normally distributed random variables, with the additional property that any finite collection of these random variables …
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Robustness in general refers to a statistic's insensitivity to deviations from its underlying assumptions (Huber and Ronchetti, 2009).
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Shifting and rescaling data to assure zero mean and unit variance.
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Refers to the probability distribution of parameters conditioned on data in Bayesian statistics.
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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…
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Combining probabilities with Bayes' Theorem, especially as used for conditional inference.
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express measurements, usually ratio, interval, ordinal or nominal scales.
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fit curves (as in linear or non-linear regression) to data.
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Refers to any statistical complication or problem due to having few data.
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the distribution of a random variable whose logarithm has a normal distribution.
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Measure of distance between distributions or variables, such as Euclidean distance between points in n-space.
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A distribution describing the time between events in a Poisson process; a continuous analogue of the geometric distribution.
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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.
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the value below which half the data or probability distribution lies - when the sample size is odd, the median is the 'middle' value of an ordered sample.
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Refers to techniques for classifying data into categories based on similarities (which can either be known previously, or learned).
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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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a graphical representation of the frequencies of a continuous variable. The variable is divided into bins and a bar is drawn for each bin, proportional to its frequency in the data.
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a method of estimating a probability distribution using estimators of a particular form.
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a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values.