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Specify the RSQUARE option in the MODEL statement of PROC LOGISTIC to get both. ... View more

Also see this useful page which mentions neural networks:    http://support.sas.com/kb/30333 ... View more

Inverse confidence limits in the context of a binary response model (logistic or probit) can be done using the INVERSECL option in PROC PROBIT.  The method used is described in the "Details: Inverse Confidence Limits" section of the PROBIT documentation. ... View more

I think you want b2+b3.  The wage for single females under your model is b0+b1.  For married females it is b0+b1+b2+b3.  So the difference in wage is b2+b3. ... View more

Rather than use a log-linked binomial model which can have the estimating problems that Jacob mentioned, you could try using a Poisson model with offset. See discussion of Zou's modified Poisson approach (and discussion of the log-linked binomial approach) in this note:    23003 - Estimating a relative risk (prevalence ratio) ... View more

It sounds to me like this is a simple logistic model applied to aggregated data.  So, you could use PROC LOGISTIC and events/trials syntax as below.  Unless the data set is small and sparse, the EXACT statement isn't necessary and would probably take too much time. proc logistic; class time; model x/n = time; run; ... View more

It may be simpler to take a nonmodeling approach if you just have one predictor (type of fruit).  This can be done with the CMH option in PROC FREQ.  For example: proc freq; table strata*FruitType*response /  cmh noprint; run; Of the three CMH statistics that are produced, the second one (Row mean scores differ) treats the row variable (FruitType) as nominal and the response as ordinal.  ... View more

If you treat this as a problem of testing agreement among 5 raters, you can use the multiple rater kappa provided by the MAGREE macro:    http://support.sas.com/kb/25006 ... View more

Unfortunately, the ODDSRATIO statement did not honor the REF= level set by the CLASS statement until the current (SAS 9.3 TS1M2) release.  Prior to that release, you canuse the LSMEANS statement with the DIFF=CONTROL, EXP, and CL options instead of the ODDSRATIO statement.  The LSMEANS statement requires that you also use the PARAM=GLM option in the CLASS statement.  For example, if variable A has values 1 and 2 and you want the odds ratio with the odds for level 1 in the denominator, the following LSMEANS statement provides the odds ratio estimate and confidence limits in the "Exponentiated" columns of the "Differences of A Least Squares Means" table. proc logistic; class a b / param=glm; model y=a b; lsmeans a / diff=control('2') exp cl; run; ... View more

You can use PROC GENMOD to estimate the poisson mean (lambda).  For example, using the portion of data you provided: data a; input LOS  Code; datalines; 4                 500 3                 500 7                 501 2                 200 8                 501 10                501 12                501 ; proc genmod;    model los = / dist=poisson;    estimate 'lambda' intercept 1;    run; The intercept reported (1.8827) is the estimated log mean.  So, exp(1.8827)=6.57 is the estimated poisson mean.  Assuming you'll want a confidence interval, the ESTIMATE statement merely repeats the estimate of the intercept which provides the mean estimate and confidence limits (4.92, 8.77). A histogram of the observed data can be done using the HISTOGRAM statement in PROC UNIVARIATE: proc univariate noprint; var los; hist los; run; ... View more