Minimum relevant variables in linear system (Min-RVLS) is a problem in mathematical optimization. Given a linear program, it is required to find a feasible...

problem Metric k-center / vertex k-center problem Minimum relevant variables in linear system Minimum spanning tree Nurse scheduling problem Ring star...

independent variables, x and z, say. In the most general case there may be one or more independent variables and one or more dependent variables at each data...

variable. As in linear regression, the outcome variables Yi are assumed to depend on the explanatory variables x1,i ... xm,i. Explanatory variables The...

a set of p {\displaystyle p} variables is the derived variable formed as a linear combination of the original variables that explains the most variance...

variables that tend to be more accurate than those based on a single measurement, by estimating a joint probability distribution over the variables for...

discontinuity design requires that all potentially relevant variables besides the treatment variable and outcome variable be continuous at the point where the treatment...

different variables. However, two variables in use at the same time cannot be assigned to the same register without corrupting one of the variables. If there...

multicollinearity refers to a situation where the predictive variables have an exact linear relationship. When there is perfect collinearity, the design...

compatible and incompatible variables. Simultaneous measurement of a complete set of compatible variables prepares the system in a unique state. The state...

hypothesized to influence it, called independent variables or explanatory variables. A functional form, often linear, is hypothesized for the postulated causal...

algorithms: linear, binary, and hashing. Linear search algorithms check every record for the one associated with a target key in a linear fashion. Binary...

squares parameter fitting, can be derived analytically in explicit form when the relevant variables are normally distributed. A normal distribution is sometimes...

causality, and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables. There...

Wilks, S. S. (1938). "Weighting systems for linear functions of correlated variables when there is no dependent variable". Psychometrika, volume 3, pages...

state variables. Stochastic control aims to design the time path of the controlled variables that performs the desired control task with minimum cost,...

the particle. In all scientific measurement, there exists a degree of error due to various factors, such as unaccounted-for variables, imperfect procedures...

does not consider the distribution of the individual variables, but rather that those variables do not exceed their respective specified limits. This...

and Method", in which they described both the linear and bull's eye printing patterns, as well as the mechanical and electronic systems needed to read...

chaos may occur also in linear systems, provided they are infinite dimensional. A theory of linear chaos is being developed in functional analysis. Quantum...

with continuous variables are normally solved more easily. Design variables are often bounded, that is, they often have maximum and minimum values. Depending...

to separate the effects of minimum wage from all the other variables that affect employment. Studies have found that minimum wages have the following positive...

Nomograms for more than three variables can be constructed by incorporating a grid of scales for two of the variables, or by concatenating individual...

linear that LTI system theory is an acceptable representation of their input-output behavior. Descriptions are given in terms of a complex variable,...

latent variables (variables thought to exist but which can't be directly observed). Additional causal connections link those latent variables to observed...

joint variations in response to unobserved latent variables. The observed variables are modelled as linear combinations of the potential factors plus "error"...

subset of multivariate normal random variables, one only needs to drop the irrelevant variables (the variables that one wants to marginalize out) from...

In machine learning, supervised learning (SL) is a paradigm where a model is trained using input objects (e.g. a vector of predictor variables) and desired...

more independent variables, also referred to as "input variables" or "predictor variables." The change in one or more independent variables is generally hypothesized...

procedure, the properties of observations are termed explanatory variables (or independent variables, regressors, etc.), and the categories to be predicted are...