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EXST-Exam01Rev

EXST-Exam01Rev - Logistic/logit model ^=e(LI[1...

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Logistic/logit model : π ^ =e α+β(LI) /[1+e α+β(LI) ]=e -3.777+0.145(8) / [1+e -3.777+0.145(8) ]= 0.073/1.073.; At π ^ = 0.50→ x=-α ^ ^ .; Rate of change= β ^ π ^ (1- π ^ ). When LI increase by 1, odds= e β^ ; Wald Statistics : (β/SE) 2 =z 2 = 6.04 with df=1 → Chi-square Table: p= 0.01. Thus, HO: β= 0 is rejected →there is effect, and model is reasonable .; Wald CI odds to a 1-unit increase in X→ e β^ ± Z α/2 (SE) = 1.16 ± 1.96 (0.059), thus CI is ( 1.044, 1.276). So, odds at LI = x + 1 are estimated between 1.044 and 1.276 times the odds at LI= x.; Odds ratio for race = e β = e 1.256 = 3.5→ odds of having sexual intercourse is (3.5) times higher for black than white.; Likelihood-ratio (LR Statistic) See SAS output (8.30), then with df=1→ p<0.01 → HO : β= 0 is rejected →there is effect of treatment on patient.; Likelihood-ratio CI for odds,(under column “Likelihood ratio 95% Conf.Limits”), find lower and upper limits (0.0425 and 0.2846). Then,exponentiate: e 0.0425 and e 02846 are(1.04, 1.33) → odds at LI=x+1 are between 1.04 and 1.33 times the odds at LI=x. If not available from SAS : Likelihood-ratio full = -2 ln (l 0 /l 1 )= -2 (LoglikelihoodReduce - LoglikelihoodFull)= (DevianceReduce -DevianceFull). Exam01-1 .For Logit/probit/binomial (identity): Deviance (0.1 - 2.8) and P>0.05 →G oodness of fit . If Deviance> 2.8 lack of fit → more categories/groups for predictors.; For normal (identity) /simple/linear model : deviance = -2 (LM -LS). LM: loglikelihood Reduce, LS: loglikelihood Simple.
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