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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Forecasting involves estimating the value or distribution of a random variable which has not yet been observed.
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a method of estimating parameters of a statistical model by choosing the parameter value that optimizes the probability of observing the given sample.
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statistical models whose primary purpose is to predict other observations of a system optimally, as opposed to models whose purpose is to test a particular hypothesis or explain …
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a high-level language and interactive programming environment developed by MathWorks. It is the foundation for a number of other tools related to statistical analysis, including Statistics T…
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Mathematical re-expression, often nonlinear, of data values. Data are often transformed either to meet the assumptions of a statistical model or to make the results of an analysis more interpretable.
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Procedures that rely on relatively few assumptions about underlying probability distributions.
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Neural networks traditionally refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks (ANN), which are composed of artificial neuro…
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concerned with modelling the time before subjects change state, typically time until death or failure. One key feature of such data is that they can be censored, that is, some sub…
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A situation where the effect of an explanatory variable may depend on the value of another explanatory variable.
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Structured data files in any format, collected together with the documentation that explains their production or use.
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a problem of judging which model from some set performs best. Popular methods include $R^2$, AIC and BIC criteria, test sets, and cross-validation. To some extent, feature selection…
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A discrete distribution defined on the non-negative integers that has the property that the mean is equal to the variance.
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The binomial distribution gives the frequencies of "successes" in a fixed number of independent "trials". Use this tag for questions about data that might be binomially distributed or for questions a…
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In hypothesis testing, the $p$-value refers to the probability of seeing a result as extreme (or more so) than the one observed, given that the null hypothesis is true. When the $p$-value is small, th…
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Refers to any model where the a random variable is related to one or more random variables by a function that is linear in a finite number of parameters.
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a field of statistics dealing with applications to economics.
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a proprietary cross-platform general-purpose statistical software package.
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our generic tag for questions seeking information about books, papers, presentations, videos of lectures, on-line tutorials, etc., regarding any subject matter that is on-topic for Cro…
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concerned about achieving intended power and size when more than one hypothesis test is performed.
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Methods and principles of selecting a subset of attributes for use in further modelling
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a resampling method to estimate the sampling distribution of a statistic.
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Refers generally to making substantive conclusions from the results of a statistical analysis.
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the square root of the variance of a random variable, an estimator thereof, or a similar measure of the spread of a batch of data.
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The expected value of a random variable; or a location measure for a sample.
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In statistics this refers to selecting an estimator of a parameter by maximizing or minimizing some function of the data. One very common example is choosing an estimator which maximizes the joint den…
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very ambiguous. Use it when the question is about sample size & the following are NOT more appropriate: [small-sample], [large-data], [power-analysis], [power], or [underdetermined].
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Experimental design, or design of experiments (DOE), is the design of any information-gathering exercises where variation is present. In statistics, controlled experiments are usually implied, which i…
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Refers to a general estimation technique that selects the parameter value to minimize the squared difference between two quantities, such as the observed value of a variable, and the expected value of…
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a machine-learning classifier based on choosing random subsets of variables for each tree and using the most frequent tree output as the overall classification.
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The probability that an event A will occur, when another event B is known to occur or to have occurred. It is commonly denoted by P(A|B).
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Problems focusing on (but not necessarily limited to) using Python for statistical computation.
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Refers to the AutoRegressive Integrated Moving Average model used in time series modeling both for data description and for forecasting. This model generalizes the ARMA model by including a term for d…
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a data reduction technique which replaces inter-correlating variables by a smaller number of continuous latent variables called factors. The factors are believed to be responsible f…
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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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concerned with assessing the probability of unknown values from known values and inferred relationships.
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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 …