lecture 14 outliers and multicollinearity
Download
Skip this Video
Download Presentation
LECTURE 14 OUTLIERS AND MULTICOLLINEARITY

Loading in 2 Seconds...

play fullscreen
1 / 10

LECTURE 14 OUTLIERS AND MULTICOLLINEARITY - PowerPoint PPT Presentation


  • 127 Views
  • Uploaded on

LECTURE 14 OUTLIERS AND MULTICOLLINEARITY. OUTLIER ANALYSIS 1. VISUAL DISPLAY 2. INTERACTIVE INSPECTION: http://www.stat.uiuc.edu/~stat100/java/guess/PPApplet.html. OUTLIERS. LEVERAGE h ii = 1/n + (Score – M x )/ x 2 (single predictor) Should be close to 1/n

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' LECTURE 14 OUTLIERS AND MULTICOLLINEARITY' - leonard-rowe


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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
lecture 14 outliers and multicollinearity
LECTURE 14OUTLIERS AND MULTICOLLINEARITY
  • OUTLIER ANALYSIS
    • 1. VISUAL DISPLAY
    • 2. INTERACTIVE INSPECTION:

http://www.stat.uiuc.edu/~stat100/java/guess/PPApplet.html

outliers
OUTLIERS
  • LEVERAGE
  • hii= 1/n + (Score – Mx)/x2 (single predictor)

Should be close to 1/n

  • Centered: h*ii= hii- 1/n
outliers1
OUTLIERS
  • Test: t(case I deleted)= [resid(i)/ 1- hij] / [MSres(i)/(1- hij )]
  • Where resid(i) = residual of Y-Ymni with case i removed
  • SPSS- take case i out, run analysis with SAVE
outliers2
OUTLIERS
  • MAHALANOBIS (Euclidean) distance of DV score from centroid of IVs
  • Cook’s D: C =  (Y – Yi)2 /[(k-1)*MSres]
  • DFFITSi = (Y – Yi) /SQRT[MSresi hii]
outliers3
OUTLIERS
  • SPSS: GENERAL LINEAR MODEL OPTIONS: ‘SAVE’

(check ‘Leverage Values’ and ‘Cooks’ to get hii and C

Plot C and h against the cases

outliers what to do
OUTLIERS – WHAT TO DO
  • DELETE
  • REVISE MODEL
  • TRANSFORM VARIABLES (LOG, SQRT, LOGIT, ARCSIN, ETC.)
  • ROBUST METHODS:
    • LTS (LEAST TRIMMED SQUARES)
    • VARIANT: WINDSORIZE (REMOVE TOP 5%, BOTTOM 5%)
  • M-estimation: weight least squares for each case by deviation from regression line
multicollinearity
MULTICOLLINEARITY
  • EXACT COLLINEARITY: One IV is predicted perfectly from another set of IVs
  • MULTICOLLINEARITY: high correlation between one IV and another or set of other IVs
multicollinearity measures
MULTICOLLINEARITY Measures
  • VIF- Variance Inflation Factor

VIF(i) = 1 / [ 1 – R2(i.1,2,3,…k)

Calculates the R-square for each predictor from all the rest of the predictors

  • TOLERANCE

= 1 / VIF

  • CONDITION INDEX

= max / min

= largest eigenvalue over smallest

critical conditions
CRITICAL CONDITIONS
  • VIF- Variance Inflation Factor > 10
  • TOLERANCE

= 1 / VIF < .10

  • CONDITION INDEX > 30
fixing multicollinearity
FIXING MULTICOLLINEARITY
  • REVISE MODEL
  • NEW DATA
  • RIDGE REGRESSION: SPSS Macro
  • PRINCIPAL COMPONENTS REGRESSION
    • STANDARDIZE PREDICTORS
    • GET PRINCIPAL COMPONENT WEIGHTS
    • CREATE NEW PRIN.COMP. SCORES, USE AS PREDICTORS
ad