Why Use Logit Model. You could use the likelihood value of each model to decide for logit vs probit. These models are specifically made for binary dependent variables and always result in 0.
Logistic Regression Model For Stock Price Movement Data Science In Finance More Info On Http Lifewaysvillage C Data Science Logistic Regression Science from www.pinterest.com
But many of the others work just as well. You could use the likelihood value of each model to decide for logit vs probit. Both have versions for binary ordinal or multinomial categorical outcomes.
Both can be used for modeling the relationship between one or more numerical or categorical predictor variables and a categorical outcome.
These models are specifically made for binary dependent variables and always result in 0. Both have versions for binary ordinal or multinomial categorical outcomes. Distribution is an S-shaped distribution function which is similar to the standard-normal distribution which results in a probit regression model but easier to work. The base of the logarithm is not important but taking logarithm of odds is.
