Is probit better than logit?
I'm exploring statistical models for binary classification and am torn between using probit and logit. I've heard they have different assumptions and strengths. I want to know if probit is superior to logit in certain situations.
Why is logit better than LPM?
I want to understand why logit is considered a better choice compared to LPM. What are the advantages of logit over LPM that make it a preferred option?
What does logit tell us?
I'm trying to understand the concept of logit. I want to know what kind of information logit provides and how it can be interpreted in the context of statistics and data analysis.
What are the advantages of logit over probit?
I'm interested in understanding the benefits of using logit over probit. Could you explain the advantages that logit offers compared to probit?
What does logit stand for?
I'm trying to understand the term 'logit'. I want to know what it stands for and its origin. Is it an abbreviation or does it have a specific meaning in statistics or another field?