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'ordinal` refers to data that have an order but not necessarily equal spacing between levels. It can also refer to ordinal logistic regression.
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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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Is a measurement of the strength and direction of the linear relationship between two variables. The covariance between $X$ and $Y$ is defined as $${\rm cov}(X,Y) = E \left[ \left( X-E(X) \right) \lef…
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Problems focusing on (but not necessarily limited to) using Python for statistical computation.
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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 target distribution. MCMC methods a…
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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…
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an observation that appears to be unusual or not well described relative to a simple characterization of a dataset.
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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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Is any random process $Y_{t}$ such that the future is conditionally independent of the past, given the present. That is the distribution of the process only depends on where the process is, not where …
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describe the results of a random experiment where each of $n$ outcomes are placed into one of $k$ nominal categories. The binomial distribution is a special case whe…
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for regression models (*q.v.*) that are nonlinear functions of the *parameters* (not the data!).
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Using (pseudo-)random numbers to simulate the random behavior of a real system.
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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…
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Stands for 'Classification And Regression Trees'. CART is a technique for developing a tree model (T) to predict categories (C) and/or continuous values (R) by recursive partitioning. It does not make…
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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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functions in the R package lme4 that fit mixed effects models (ie, models that include fixed & random effects). These models can be non-linear in the sense that the…
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a weighted average of all possible values a random variable can take on, with the weights equal to the probability of taking on that value. For a discrete ra…
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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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Inference, in a statistical context, refers to drawing conclusions about a population from information about a sample from that population.
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PDF stands for Probability Density Function (as compared to CDF for Cumulative Distribution Function). The PDF of a variable gives the likelihood for each value of a continuous variable. Use this tag …
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Refers to the property of a random process to have non-constant variance along some continuum. This most commonly presents in regression where the error variance increases as a function of one or more…
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A way of re-expressing data to make their values lie between 0 and 1 (or 0% and 100%).
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a commercial spreadsheet program created by Microsoft.
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