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REGRES SION DIAGNOSTI CS

REGRES SION DIAGNOSTI CS. Problems. Influentials and outliers heteros c edasticit y auto c or relation. Regrese a její problémy. Mul t ic olinearit y – relationship of independent variables. RE S IDUA LS. Re s idua ls - review. Unstandardized re s idua ls H = hat matrix

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REGRES SION DIAGNOSTI CS

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  1. REGRESSION DIAGNOSTICS

  2. Problems • Influentials and outliers • heteroscedasticity • autocorrelation

  3. Regrese a její problémy • Multicolinearity – relationship of independent variables

  4. RESIDUALS

  5. Residuals - review • Unstandardized residuals H = hat matrix • Predicted residuals

  6. Rezidua - review • Standardized residuals • Jackknife residuals

  7. Influentials • If omitted from computation big change in regression coeffs can be found. • Goal: to find and exclude

  8. Influentials -diagnosis • DFBETA(-i)=b-b(-i) Rule of thumb: Problém if NDFBETA>2/√n Note : DFFIT and NDFFIT problem if NDFFIT>2/√(n/p)

  9. Heteroscedasticity • Assumption for regression: variance of error is the same for all values of indep. variable • Checking: Charts for residuals vs. ind. vars • Tests - Glejser, Goldfeld-Quandt • Solution: weighted LS

  10. Glejser’stest • Model for residuals on ind. vars :

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