Chellai Fatih

The Logistic Regression is appropriate for binary data, and Survival Analysis is appropriate for time to event data. There are similarities between the two and Logistic Regression can be used to analyze time-to-event data although it's not ideal.

Chellai Fatih

Also, the

linear regression model can be analysis of life times

then the survival regression look like Cox’s model.

Best regardes

As usual this depends on your research question(s). One can think of cases where the three could be combined in one paper But not on the same data. As noted by Salwa A. Mousa Binomial Logistic regression is about binary dependent variables, Cox regression is about time to event data. Both of these are linear statistical models. I presume you meant by your question OLS regression, where the DV is continuous. Again if your data is appropriate the answer is yes. For example, the attached uses more than one method, though the purposes are a bit different than I suspect you have in mind. Best wishes, David Booth

Thank you so much dear professor@David Eugen, yes I want to applied them on the same data set, and see how the explanatory variables behave when we change the target and the scale of the dependent variable.

Thank you also for Dr @salwa, yes I know the difference dear professor.

Best wishes for you

I agree with everyone else about needing to know your research question and the outcome measure(s) you want to study. I'll add that with your mention of OLS regression and logistic regression, I wonder if ordinal logistic regression would also be something to consider because it is midway between the two. You could divide your outcome into tertiles or quantiles, say, and look at which factors predict upward or downward movement through them. I'll finish by saying that both methods -- transforming a continuous regression into either and ordinal logistic regression or logistic regression -- lose information, so you should have a good reason for doing them that outweighs that con.

Thank you dear professor Linda Hermer , I want to modelise the age at first marriage; for that I want to estimate the effect of explantory variables on; statisticaly, this variable can be measured as :

My question dears is: can we applied the 3 methods on the same data set, and see what are the effects of explanatory variables on age at first marriage in the continuos scale, and dichotomous scale; and is it a logical appraoch ?

best Regards

Chellai Fatih do notice that the DVs are of different types so all methodswill only work with am paricular DV type and hence can't be compared on the same data.

Best, David Booth