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.
concerned about achieving intended power and size when more than one hypothesis test is performed.
A vast area which includes generating results from computer models.
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.
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…
High-level language and interactive programming environment for numerical computing developed by MathWorks.
a proprietary cross-platform general-purpose statistical software package.
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…
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…
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…
Usage and meaning of specific technical words/concepts in statistics.
the actual values minus the predicted values. Many statistical models make assumptions about the error, which is estimated by the residuals.
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.
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…
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…
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.
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 …
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.
a formalization of relationships between variables in the form of mathematical equations. The model is statistical as the variables are not deterministically but stochastically …
A regularization method for regression models that shrinks coefficients towards zero, making some of them equal to zero. Thus lasso performs feature selection.
'Classification And Regression Trees'. CART is a popular data mining technique.
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…
A binary variable takes one of two values, typically coded as "0" and "1".
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…
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…
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…
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…
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…
Usually "normalization" means re-expressing data to make values lie within a specified range.
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…
collect a sample from a population. Surveying often refers to sampling of human populations and is primarily done by administering questionnaires or interviewing indivi…
Using (pseudo-)random numbers and the Law of Large Numbers to simulate the random behavior of a real system.
Methods focused on contrasting and combining results from different studies, in the hope of increasing precision and external validity.
A stochastic process with the property that the future is conditionally independent of the past, given the present.
Goodness of fit tests indicate whether or not it is reasonable to assume that a random sample comes from a specific distribution.
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