Prediction of a binary variable I am establishing a model for prediction of a binary variable (Yes/No) depending on three continuous variables ($A$,$B$,$C$). I applied logistic regression analysis for a learning dataset vith the Tanagra software, and the results were good with high prediction accuracy.
My question is: is it possible to get the probabilities of the prediction via logistic regression? Something like (0.7 Yes)? If not, what test do I have to use to get such a result?
 A: A binary logistic regression is generally used for fitting a model to a binary output, but formally the results of logistic regression are not themselves binary, they are continuous probability values (pushed to zero or 1 by a logit transformaion, but continuous between 0 and 1 nonetheless). It sounds like the software you are using is rounding the output for you, which you don't want. Here's a simple example demonstrating how you could accomplish this in R, since it sounds like you are amenable to trying new software:
# generate sample data
set.seed(123)
x = rnorm(100) 
y= as.numeric(x>0)

# let's shuffle a handful so we don't fit a perfect model
ix = sample(1:100, 10)
y[ix]= 1-y[ix]

# Let's take a look at our observations
df = data.frame(x,y)
plot(df)


# Build the model
m = glm(y~x, family=binomial(logit), data=df)

# Look at results
summary(m)

# generate predictions. Here, since I'm not passing in new data
# it will use the training data set to generate predictions
y.pred = predict(m, type="response")
plot(x, y.pred, col=(round(y.pred)+1))


A: Yes, you can get the predicted probability that an observation is yes from a logistic regression model.  If you have the estimated coefficients from your model fit, you can use those to get the predicted probabilities thusly:
$$
\widehat{p(y_i=1)} = \frac{\exp(\hat\beta_0 + \hat\beta_AA_i + \hat\beta_BB_i + \hat\beta_CC_i)}{1+\exp(\hat\beta_0 + \hat\beta_AA_i + \hat\beta_BB_i + \hat\beta_CC_i)}
$$
