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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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concerned about achieving intended power and size when more than one hypothesis test is performed.
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A vast area which includes generating results from computer models.
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the correlation of a series of data with itself at some lag. This is an important topic particularly in the analysis of time-series data.
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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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High-level language and interactive programming environment for numerical computing developed by MathWorks.
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a proprietary cross-platform general-purpose statistical software package.
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a machine-learning library for Python that provides simple and efficient tools for data analysis and data mining, with a focus on machine learning. It is accessible to everybody and re…
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Data mining uses methods from artificial intelligence in a database context to discover previously unknown patterns. As such, the methods are usually unsupervised. It is closely related but not identi…
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a quantity used to measure the strength and direction of the linear relationship between two variables. The covariance is unscaled, & thus often difficult to interpret; when scaled by th…
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Usage and meaning of specific technical words/concepts in statistics.
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the actual values minus the predicted values. Many statistical models make assumptions about the error, which is estimated by the residuals.
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Probability density function (PDF) of a continuous random variable gives the relative probability for each of its possible values. Use this tag for discrete probability mass functions (PMFs) too.
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Markov Chain Monte Carlo (MCMC) refers to a class of methods for generating samples from a target distribution by generating random numbers from a Markov Chain whose stationary distribution is the tar…
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Statistical analysis of datasets comprising several levels of hierarchy (e.g., students nested in classes nested in schools or hierarchical forecasting). For questions about mixed models use [mixed-mo…
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When the data present lack of information (gaps), i.e., are not complete. Hence, it is important to consider this feature when performing an analysis or test.
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Refers to the standard deviation of the sampling distribution of a statistic calculated from a sample. Standard errors are often required when forming confidence intervals or testing hypotheses about …
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a type of neural network in which only subsets of possible connections between layers exist to create overlapping regions. They are commonly used for visual tasks.
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a formalization of relationships between variables in the form of mathematical equations. The model is statistical as the variables are not deterministically but stochastically …
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A regularization method for regression models that shrinks coefficients towards zero, making some of them equal to zero. Thus lasso performs feature selection.
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'Classification And Regression Trees'. CART is a popular data mining technique.
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an observation that appears to be unusual or not well described relative to a simple characterization of a dataset. A discomfiting possibility is that these data come from a different po…
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A binary variable takes one of two values, typically coded as "0" and "1".
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A stochastic process describes evolution of random variables/systems over time and/or space and/or any other index set. It has applications in areas such as econometrics, weather, signal processing, e…
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a dimensionality reduction latent variable technique which replaces inter-correlating variables by a smaller number of continuous latent variables called factors. The factors are be…
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independent when information on some of them tells you nothing about the probability of occurrence (/ distribution) of the others. Please DO NOT use this tag for indep…
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A strictly stationary process (or time series) is one whose joint distribution is constant over time shifts. A weakly stationary (or covariance stationary) process or series is one whose mean and cova…
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a semi-parametric method for survival analysis. No distributional form needs to be assumed, only that the effect of one-unit increase in a covariate is a constan…
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Usually "normalization" means re-expressing data to make values lie within a specified range.
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Parameters associated with the particular levels of a covariate are sometimes called the “effects” of the levels. If the levels that are observed represent a random sample from the set of all possible…
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collect a sample from a population. Surveying often refers to sampling of human populations and is primarily done by administering questionnaires or interviewing indivi…
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Using (pseudo-)random numbers and the Law of Large Numbers to simulate the random behavior of a real system.
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Methods focused on contrasting and combining results from different studies, in the hope of increasing precision and external validity.
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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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Goodness of fit tests indicate whether or not it is reasonable to assume that a random sample comes from a specific distribution.
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a method to partition data into clusters by finding a specified number of means, k, s.t. when data are assigned to clusters w/ the nearest mean, the w/i cluster sum of squares is minimized
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Non-constant variance along some continuum in a random process.