Similar Threads
1. logistic regression for continuous dependent variable
Dear All,
Is it possible to run logistic regression on a dependent variable
carrying continuous values?
If so,
 are there any theoretical violations?
 do the model estimations become biased?
 any convergence problems with the iterative logistic algorithm?
Many thanks in advance for any insight that you may be able to
provide.
Kind regards,
Kea
2. LOGISTIC REGRESSION OF NOMINAL DEPENDENT (Y) VARIABLE AND ORDINAL PREDICTOR (X) VARIABLE
3. Logistic Regression and Unequal Distribution of Dependent Variable
Hello, I'm preparing to run a logit model predicting the odds of NOT
testing for an STD.
As you can see from the table below, 2934 (about 86%) of respondents have my outcome of interest (i.e., have not tested for an STD).
I realized that because of this unequal unequal distribution of the dependent variable, all crosstabulations have higher proportions within the untested category of those who have not been tested, regardless of the distribution of the other variable.
I have a feeling that these could bias my estimates in a way  since the nottested category seemed overestimated. For example, given the unequal groupings, I think I am only restricted to modeling failure to test (the zero outcome), as modeling for ever tested (1) could lead to unstable estimates.
So my question is it worth producing any crosstabs showing the distribution of sociodemographic variables within my outcome of interest?
What possible impact will this have on my logistic model, and what can I do about it? Thanks  Yawo
===================>
Table 1:
RECODE of 
V827 
(Last 
test was 
on your 
own, 
offered  RECODE of V501 (Current
or  marital status)
required)  0 1 2 Total
+
Not Test  99.37 81.1 99.08 88.75
 514 1563 857 2934

Asked fo  .2992 1.015 .2525 .6992
 2 18 2 22

Offered  .2523 17.63 .1184 10.24
 3 427 1 431

Test Req  .0816 .253 .5512 .3114
 1 5 2 8

Total  100 100 100 100
 520 2013 862 3395

Key: column percentages
number of observations

Table 2:
RECODE of 
V827 
(Last 
test was 
on your 
own, 
offered  RECODE of V106 (Highest
or  educational level)
required)  0 1 2 Total
+
Not Test  83.34 96.84 89.9 88.75
 724 273 1937 2934

Asked fo  .2094 1.662 .777 .6992
 2 4 16 22

Offered  16.37 1.497 8.887 10.24
 209 3 219 431

Test Req  .0785 0 .4358 .3114
 1 0 7 8

Total  100 100 100 100
 936 280 2179 3395

Key: column percentages
number of observations
4. Dependent variable in logistic regression
5. Dependent variable is ratio of continuous values
Hi.
I have a situation where the target variable I want to model takes on
values between 0 and 1, endpoints inclusive, because it is a ratio of two
continuous quantities (e.g., actual sales / sales target)...
Is there a "preferred" method for modeling ratios of continuous quantities,
and if so, is it available in SAS?
Someone suggested the use of the PROC LOGISTIC events/trials syntax, but
the SAS documentation for that really seems to stress binary outcome
trials...
Is it legitimate to use PROC LOGISTIC events/trials for continuous
numerator and denominator?
An econometrics textbook ("Econometric Analysis" by W.H. Greene, 5th
edition, PrenticeHall) suggests Minimum ChiSquared Estimation (or MCSE 
don't tell Microsoft!) for proportions; it looks like that discussion was
motivated by proportions of binary outcomes, but I think the equations
still work in the case of continuous numerator and denominator. However, a
search of support.sas.com didn't turn up any procedures that support the
MCSE methodology. One weakness of MCSE is that the estimation only works
when the proportion does not take on the extreme values of 0 or 1. In such
cases, it seems that the suggested workaround is to add or subtract a
small constant...
Is there a SAS procedure that does minimum chisquared estimation for
proportions?
I could also transform the ratio from the unit interval to the entire line
with the logit transform, log(y/(1y)), and then perform a standard
regression. Again, I'd have to smudge the extreme values before performing
the transformation...
Is it legitimate to logit transform the ratio after slightly modifying the
extreme values, and then do OLS?
Thanks, as ever, for your indulgence...
 TMK 
6. Logistic Regression and Unequal Distribution of Dependent
7. Stat QuestionBucket a continuous variableIs it still a continuous variable
8. Interaction between categorical and continuous variables in regression model