Linear Model: A linear model specifies a linear relationship between a dependent variable and n independent variables: y = a0 + a1 x1 + a2 x2 + ¼+ an xn, where y is the dependent variable, {xi} are independent variables, {ai} are parameters of the model. For example, consider...
View Full DescriptionLogit and Probit Models: Logit and probit models postulate some relation (usually - a linear relation) between nonlinear functions of the observed probabilities and unknown parameters of the model. Logit and probit here are nonlinear functions of probability. See also: Logit Models , Probit Models . Browse Other Glossary Entries
View Full DescriptionLogit Models: Logit models postulate some relation between the logit of observed probabilities (not the probabilities themselves), and unknown parameters of the model. For example, logit models used in logistic regression postulate a linear relation between the logit and parameters of the model. The major reason for using logits, as...
View Full DescriptionLoglinear models: Loglinear models are models that postulate a linear relationship between the independent variables and the logarithm of the dependent variable, for example: log(y) = a0 + a1 x1 + a2 x2 ... + aN xN where y is the dependent variable; xi, i=1,...,N are independent variables, and...
View Full DescriptionMixed Models: In mixed effects models (or mixed random and fixed effects models) some coefficients are treated as fixed effects and some as random effects. See fixed effects for detailed explanations of the concepts "random effects" and "fixed effects". Browse Other Glossary Entries
View Full DescriptionProbit Models: Probit models postulate some relation between the probit of the observed probability, and unknown parameters of the model. The most common example is the model probit(p) = a + b x which is equivalent to : p = F(a + b x) where F() is the...
View Full DescriptionProportional Hazard Model: Proportional hazard model is a generic term for models (particularly survival models in medicine) that have the form L(t | x1, x2, ¼, xn) = h(t) exp(b1 x1 + ¼+ bn xn), where L is the hazard function or hazard rate, {xi} are covariates, {bi} are...
View Full DescriptionRandom Effects: The term "random effects" (as contrasted with "fixed effects") is related to how particular coefficients in a model are treated - as fixed or random values. Which approach to choose depends on both the nature of the data and the objective of the study. See fixed effects for...
View Full DescriptionWeb Analytics: Statistical or machine learning methods applied to web data such as page views, hits, clicks, and conversions (sales), generally with a view to learning what web presentations are most effective in achieving the organizational goal (usually sales). This goal might be to sell products and services on a...
View Full DescriptionNetwork Analytics: Network analytics is the science of describing and, especially, visualizing the connections among objects. The objects might be human, biological or physical. Graphical representation is a crucial part of the process; Wayne Zachary's classic 1977 network diagram of a karate club reveals the centrality of two individuals, and...
View Full DescriptionStructural Equation Modeling: Structural equation modeling includes a broad range of multivariate analysis methods aimed at finding interrelations among the variables in linear models by examining variances and covariances of the variables. Path analysis , for example, is a method of structural equation modeling. Structural equation models are usually represented...
View Full DescriptionVector Autoregressive Models: Vector autoregressive models describe statistical properties of vector time series . Vector autoregressive models generalize the models used in ordinary autoregression . Consider a vector time series : V(1), V(2), ... In general, vector autoregressive models assume the some functional relation between the current value V(i)...
View Full DescriptionAnalysis of Commonality: Analysis of commonality is a method for causal modeling . In a simple case of two independent variables x1 and x2 , for example, analysis of commonality posits three sources of causation, described by three latent variables: u1 and u2 , which reflect the unique contributions of...
View Full DescriptionAnalysis of Covariance (ANCOVA): Analysis of covariance is a more sophisticated method of analysis of variance. It is based on inclusion of supplementary variables (covariates) into the model. This lets you account for inter-group variation associated not with the "treatment" itself, but with covariate(s). Suppose you analyze the results of...
View Full DescriptionANCOVA: See Analysis of covariance Browse Other Glossary Entries
View Full DescriptionCanonical Correlation Analysis: The purpose of canonical correlation analysis is to explain or summarize the relationship between two sets of variables by finding a linear combinations of each set of variables that yields the highest possible correlation between the composite variable for set A and the composite variable for set...
View Full DescriptionCanonical Discriminant Analysis: See Multiple discriminant analysis . Browse Other Glossary Entries
View Full DescriptionCanonical root: See discriminant function . Browse Other Glossary Entries
View Full DescriptionCanonical variates analysis: Several techniques that seek to illuminate the ways in which sets of variables are related one another. The term refers to regression analysis , MANOVA , discriminant analysis , and, most often, to canonical correlation analysis . Browse Other Glossary Entries
View Full DescriptionCausal analysis: See causal modeling . Browse Other Glossary Entries
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