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regressionlogistic-regression
Tashaho
Tashaho

Reputation: 163

Difference Between Logit Models and Logistic Regression?

I know these two model has different equation, but I am not sure why people use logistic model instead of logit model and vice versa? What is the main reason behind it? If my response variable is a decision variable(yes,no), which model would be better here and why?

Upvotes: 3

Views: 3857

Answers (1)

vestland
vestland

Reputation: 61104

If you take a look at stats.idre.ucla.edu, you'll see that it's the same thing:

Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables.

To expand on that, you'll typically use a logistic model to predict the probability of a binary event to occur or not. And yes, if your response variable is a decision variable (yes/no), you can use a Logistic Regression approach. Most often it will be useful to recode yes/no to 1 or 0.

You're not mentioning any specific tools here, but if you're using R you can easily set up a logistic model using glm():

model <- glm(outcome~X1+x2)

Here, outcome is your decision variable and X1 and X2 are your predictor variables.

Upvotes: 1

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