jointly dependent variables
1Dependent and independent variables — The terms dependent variable and independent variable are used in similar but subtly different ways in mathematics and statistics as part of the standard terminology in those subjects. They are used to distinguish between two types of quantities… …
2Errors-in-variables models — In statistics and econometrics, errors in variables models or measurement errors models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors… …
3Regression analysis — In statistics, regression analysis is a collective name for techniques for the modeling and analysis of numerical data consisting of values of a dependent variable (response variable) and of one or more independent variables (explanatory… …
4Factor analysis — is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved, uncorrelated variables called factors. In other words, it is possible, for example, that variations in …
5Correlation — In probability theory and statistics, correlation, (often measured as a correlation coefficient), indicates the strength and direction of a linear relationship between two random variables. In general statistical usage, correlation or co relation …
6Mutually exclusive events — For the programming algorithms, see Mutual exclusion. In layman s terms, two events are mutually exclusive if they cannot occur at the same time. An example is tossing a coin once, which can result in either heads or tails, but not both. In the… …
7Bayesian multivariate linear regression — Consider a collection of m linear regression problems for n observations, related through a set of common predictor variables {x {c}}, and a jointly normal errors {epsilon {c}} ::y {1} = eta {1} x {1} + epsilon {1},,:y {c} = eta {c} x {c} +… …
8Sensitivity analysis — (SA) is the study of how the variation (uncertainty) in the output of a mathematical model can be apportioned, qualitatively or quantitatively, to different sources of variation in the input of a model Saltelli, A., Ratto, M., Andres, T.,… …
9Cluster-weighted modeling — In statistics, cluster weighted modeling (CWM) is an algorithm based approach to non linear prediction of outputs (dependent variables) from inputs (independent variables) based on density estimation using a set of models (clusters) that are each …
10Multivariate probit — In statistics and econometrics, the multivariate probit model is a generalization of the probit model used to estimate several correlated binary outcomes jointly. For example, if it is believed that the decisions of sending at least one child to… …
