1 / 6

Psychology 202b Advanced Psychological Statistics, II

Psychology 202b Advanced Psychological Statistics, II. February 3, 2011. Overview. Multivariate data simulation Added variable plots (review) Partial correlation The problem of collinearity Regression diagnostics: Review of assumption checking Outliers Influence and leverage.

varana
Download Presentation

Psychology 202b Advanced Psychological Statistics, II

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Psychology 202bAdvanced Psychological Statistics, II February 3, 2011

  2. Overview • Multivariate data simulation • Added variable plots (review) • Partial correlation • The problem of collinearity • Regression diagnostics: • Review of assumption checking • Outliers • Influence and leverage

  3. What does “control” mean? • Controlling or holding constant • Partial relationships and the added variable plot

  4. Collinearity • The problem of collinearity • Formal definition: • Two predictors X1and X2 are collinear if there exist constants c1, c2, and c0 such that c1X1 + c2X2 = c0. • More generally, a set of k predictors is collinear if c1X1 + c2X2 + … + ckXk = c0. • Collinearity is not synonymous with correlation among the predictors.

  5. Why is collinearity a problem? • Hocking and Pendelton’s picket fence. • Implication: when a set of predictors is approximately collinear, estimation becomes unstable and standard errors become large.

  6. Diagnosing collinearity • Correlations may be diagnostic if the data are multivariate normal. • The condition number: • An alternative form removes column means from X. Other options eliminate the intercept column or use a correlation metric.

More Related