Logistic regression is appropriate when you have a continuous X variable, and a categorical Y variable. For example, if X is temperature, and Y is pass/fail status. Among other things, logistic regression will compute the probability that you get a pass (or a fail) at a given temperature. ... View more

Whenever a p value is less than 0.05, this indicates that the quantity being tested is statistically significant (at the alpha=0.05 level). In layman's terms, this particular arrangement of data that is tested was unlikely to have happened by random chance, it is probably a "real" effect. You cannot do regression on this data. You need to have continuous X and continuous Y to do a regression. ... View more

With regards to the Intercept(s), I would say "No". Leave them in the model, even if they are not statistically significant. (I expect others to disagree with this, but that is my position on the matter) You might want to read "Analysis of Messy Data, Volume 1, Designed Experiments" by Milliken and Johnson. Even though yours is not a designed experiment, they talk about relevant issues in Chapter 9. In fact, they speak of the "Means Model", which is a distinctly different parameterization than the model you get through SAS. In the "Means Model", all these issues go away. There is a distinct coefficient for the intercept of Group A, and a distinct coefficient for the intercept of Group B. And then, it doesn't matter whether you set A to be 0 and B to be 1, or the other way around. ... View more

Sir, we are creating a regression equation for prediction, not for comparison of two groups. Okay, then why the concern about the different intercepts? As you said, the models are equivalent. Either will give you the same predicted values. As I have pointed out, and now as Steve seems to be pointing out, you can create models for prediction, or you can create models for understanding the individual terms (or both). Do NOT confuse the two. If you want a predictive model, then you choose either, and you report the Overall F as its level of significance. If you want to understand the individual terms, you report the tests of the individual model coefficients, with appropriate interpretation. (and of course you can do both) You keep wandering back and forth between obtaining predictive model, and obtaining understanding of the individual terms. ... View more

Yes, the equations are equivalent. The parts of the equation are not equivalent. But since c0 is not significant, how can we adopt the second equation? It's just as valid as the first equation. You continue to confuse the validity of the equation, with the meaning of individual terms. It is up to you to understand how to interpret it properly. Perhaps instead of reporting intercepts, which is causing this confusion, you should be reporting the value, and the statistical significance, of the delta between group A and group B, which I think is simply c0-b0. That seems like a better quantity to report. ... View more

They are both correct! As I already explained. The interecepts b0 and c0 are not measuring the same thing Your equations leave out the term that accounts for the main effect of changing from group a to group b or vice versa So first case is really Y = b0 + b1*(group=B) + b2*x1 + b3*x2 + ... and c0 = b0 + b1*(group=B) and b0 = c0 + c1 * (group=A) <=== c0 is not equal to b0, they are to be interpreted differently, they measure different things ... View more

When parameterization of your model changes, the meaning of your parameters changes, and thus the statistical significance can change as well. So in the first case, the intercept has the meaning "what is the Y when all your continuous variables are 0 at group A" and in the second case, the intercept has the meaning "what is the Y when all your continuous variables are zero at group B". ... View more

What if either of the tables have duplicates by claim_no and differeing dates may be within the same claim_ no?? This is an incomplete problem specification, in my opinion, so I can't answer unless you give us more information. So we have duplicates and different dates ... then what is the desired outcome? ... View more

Assuming the data is sorted, I would just do a merge. Something like this data c;      merge a(in=in1) b(in=in2);      by claim_nbr;      if in2 then delete; run; ... View more