Tags

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.

for any *on-topic* question that (a) involves `R` either as a critical part of the question or expected answer, & (b) is not *just* about how to use `R`.
25063 questions
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
24019 questions
Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervis…
16717 questions
data observed over time (either in continuous time or at discrete time periods).
12054 questions
A probability provides a quantitative description of the likely occurrence of a particular event.
10002 questions
inconsistent with a given hypothesis rather than being an effect of random fluctuations.
a mathematical description of probabilities or frequencies.
A routine exercise from a textbook, course, or test used for a class or self-study. This community's policy is to "provide helpful hints" for such questions rather than complete answers.
a broad class of computational models loosely based on biological neural networks. They encompass feedforward NNs (including "deep" NNs), convolutional NNs, recu…
a method of statistical inference that relies on treating the model parameters as random variables and applying Bayes' theorem to deduce subjective probability statements about t…
Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
Mathematical theory of statistics, concerned with formal definitions and general results.
the problem of identifying the sub-population to which new observations belong, where the identity of the sub-population is unknown, on the basis of a training set of dat…
A measure of the degree of linear association among a pair of variables.
Statistical significance refers to the probability that, if, in the population from which this sample were drawn the true effect were 0 (or some hypothesized value) a test statistic as extreme or more…
The normal, or Gaussian, distribution has a density function that is a symmetrical bell-shaped curve. It is one of the most important distributions in statistics. Use the [normality] tag for asking ab…
Mixed (aka multilevel or hierarchical) models are linear models that include both fixed effects and random effects. They are used to model longitudinal or nested data.
Regression that includes two or more non-constant independent variables. Also known as multivariable regression.
ANOVA stands for ANalysis Of VAriance, a statistical model and set of procedures for comparing multiple group means. The independent variables in an ANOVA model are categorical, but an ANOVA table can…
an interval that covers an unknown parameter with $(1-\alpha)\%$ confidence. Confidence intervals are a frequentist concept. They are often confused with credible intervals wh…
a programming language commonly used for machine learning. Use this tag for any *on-topic* question that (a) involves `Python` either as a critical part of the question or expected answer, &…
A generalization of linear regression allowing for nonlinear relationships via a "link function" and for the variance of the response to depend on the predicted value. (Not to be confused with "genera…
the task of partitioning data into subsets of objects according to their mutual "similarity," without using preexisting knowledge such as class labels. [Clustered-standard-errors a…
The expected squared deviation of a random variable from its mean; or, the average squared deviation of data about their mean.
a special case of [prediction], in the context of [time-series].
Categorical (also called nominal) data can take on a limited number of possible values called categories. Categorical values "label", they do not "measure". Please use [ordinal-data] tag for discrete …
A test for comparing the means of two samples, or the mean of one sample (or even parameter estimates) with a specified value; also known as the "Student t-test" after the pseudonym of its inventor.
Repeatedly withholding subsets of the data during model fitting in order to quantify the model performance on the withheld data subsets.
a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much v…
too general; please provide a more specific tag. For questions about the properties of specific estimators, use [estimators] tag instead.
a method of estimating parameters of a statistical model by choosing the parameter value that optimizes the probability of observing the given sample.
Constructing meaningful and useful graphical representations of data. (If your question is only about how to get particular software to produce a specific effect, then it is likely not on topic here.)
R packages used for fitting linear, generalized linear and nonlinear mixed effects models. For general questions about mixed models use [mixed-model] tag.
Creating samples from a well-specified population using a probabilistic method and/or producing random numbers from a specified distribution. As this tag is ambiguous, please consider [survey-sampling…
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…
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 …
1
2 3 4 5
53