Regression

Regression (Multivariate Methods) (GWU EMSE-271)
Index | Topics (Logical Lectures) | Lectures | Problems | Readings | Nomenclature | Concepts

"In statistics, **regression analysis** includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. ..."

"Regression analysis is widely used for prediction. ..." "In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables"

"Regression models for prediction are often useful even when the assumptions are moderately violated, although they may not perform optimally. However when carrying out inference using regression models, especially involving small effects or questions of causality based on observational data, regression methods must be used cautiously as they can easily give misleading results." Wikipedia

However, from the Lattin and EMSE 271 (slide 184) viewpoint, "A regression model is a linear combination of independent variables that corresponds as closely as possibe to the dependent varialbe" (only one dependent variable). Regression analysis is used to for: Description, Inference and Prediction. - Lattin

The covariance matris is "used to make inferences about the values of the regression parameters." (Correlation) - EMSE 271, fall 2009, Slide 195

Underlying Assumptions ([|Wikipedia]); assumptions for "regression analysis, Lattin, Chapter 3)


 * Performing a Regression Analysis**


 * Sources:**
 * Regression analysis. (2009, December 1). In //Wikipedia, The Free Encyclopedia//. Retrieved 19:37, December 1, 2009, from []
 * Analyzing Multivariate Data, by James Lattin, Douglas Carroll and Green ([|Amazon]), page 38
 * EMSE 271, Fall 2009