39 relations: Abscissa and ordinate, Bias (statistics), Blocking (statistics), Cartesian product, Confounding, Control variable, Covariance, Data mining, Dependent and independent variables, Design of experiments, Econometrics, Errors and residuals, Experiment, Feature (machine learning), Function (mathematics), Goodness of fit, Hypothesis, Linear model, Machine learning, Manifold, Mathematical model, Medical statistics, Multivariable calculus, Multivariate statistics, Omitted-variable bias, Pattern recognition, Prediction, RapidMiner, Reliability engineering, Risk factor, Set (mathematics), Set theory, Simulation, Statistical model, Stochastic, Subset, Supervised learning, Test data, Vector-valued function.
In mathematics, the abscissa (plural abscissae or abscissæ or abscissas) and the ordinate are respectively the first and second coordinate of a point in a coordinate system.
Statistical bias is a feature of a statistical technique or of its results whereby the expected value of the results differs from the true underlying quantitative parameter being estimated.
In the statistical theory of the design of experiments, blocking is the arranging of experimental units in groups (blocks) that are similar to one another.
In set theory (and, usually, in other parts of mathematics), a Cartesian product is a mathematical operation that returns a set (or product set or simply product) from multiple sets.
In statistics, a confounder (also confounding variable, confounding factor or lurking variable) is a variable that influences both the dependent variable and independent variable causing a spurious association.
The control variable (or scientific constant) in scientific experimentation is the experimental element which is constant and unchanged throughout the course of the investigation.
In probability theory and statistics, covariance is a measure of the joint variability of two random variables.
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
In mathematical modeling, statistical modeling and experimental sciences, the values of dependent variables depend on the values of independent variables.
The design of experiments (DOE, DOX, or experimental design) is the design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation.
Econometrics is the application of statistical methods to economic data and is described as the branch of economics that aims to give empirical content to economic relations.
In statistics and optimization, errors and residuals are two closely related and easily confused measures of the deviation of an observed value of an element of a statistical sample from its "theoretical value".
An experiment is a procedure carried out to support, refute, or validate a hypothesis.
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed.
In mathematics, a function was originally the idealization of how a varying quantity depends on another quantity.
The goodness of fit of a statistical model describes how well it fits a set of observations.
A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon.
In statistics, the term linear model is used in different ways according to the context.
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.
A mathematical model is a description of a system using mathematical concepts and language.
Medical statistics deals with applications of statistics to medicine and the health sciences, including epidemiology, public health, forensic medicine, and clinical research.
Multivariable calculus (also known as multivariate calculus) is the extension of calculus in one variable to calculus with functions of several variables: the differentiation and integration of functions involving multiple variables, rather than just one.
Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable.
In statistics, omitted-variable bias (OVB) occurs when a statistical model incorrectly leaves out one or more relevant variables.
Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning.
A prediction (Latin præ-, "before," and dicere, "to say"), or forecast, is a statement about a future event.
RapidMiner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.
Reliability engineering is a sub-discipline of systems engineering that emphasizes dependability in the lifecycle management of a product.
In epidemiology, a risk factor is a variable associated with an increased risk of disease or infection.
In mathematics, a set is a collection of distinct objects, considered as an object in its own right.
Set theory is a branch of mathematical logic that studies sets, which informally are collections of objects.
Simulation is the imitation of the operation of a real-world process or system.
A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of some sample data and similar data from a larger population.
The word stochastic is an adjective in English that describes something that was randomly determined.
In mathematics, a set A is a subset of a set B, or equivalently B is a superset of A, if A is "contained" inside B, that is, all elements of A are also elements of B. A and B may coincide.
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.
Test data is data which has been specifically identified for use in tests, typically of a computer program.
A vector-valued function, also referred to as a vector function, is a mathematical function of one or more variables whose range is a set of multidimensional vectors or infinite-dimensional vectors.
Covariate, Dependant variable, Dependent Variable, Dependent variable, Dependent variables, Explained variable, Explanatory variable, Explanatory variables, Exposure variable, Extraneous Variable, Extraneous variable, Extraneous variables, Independant Varrible, Independant variable, Independant varrible, Independent Variable, Independent and dependent variables, Independent and independent variable, Independent factor, Independent risk factor, Independent risk factors, Independent variable, Independent variable(s), Independent variables, Manipulated Varrible, Manipulated variable, Manipulated varrable, Manipulated varrible, Predicted variable, Regional Dummy, Regional dummies, Regional dummy, Regressand, Regressor, Regressors, Responding variable, Responding varrable, Response variable, Target variable, With respect to, X variable, Y variable.