{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

EXST-Exam01Rev

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

This preview shows pages 1–2. Sign up to view the full content.

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.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern