Name derived from the Logit Transformation

Differences from OLS

- Customer will pay/default on loan (credit Risk)Differences from OLS

- Used for predicting the outcome of a binary dependent variable (yes or no)
- i.e., the DV has to be a Nominal variable restricted to only 2 states

- Customer will respond/ignore the offer (Marketing Response)

- Customer will churn/stay loyal (Telecom etc.)

- Uses a Logit transformation on the DV to fit a linear regression model.

**Example - Hours Studied to passing**

- We can study how probability of passing changes as per the hours studies using joint Probability distribution.

- Can we train a regression model on this relationship.

**Logistic Regression - Concepts**

- Model the PROBABILITY of an event-rather than a measure

- Need to create a dependent variables as a probability range, requires a transformation from the binary nominal variable in dataset.

- LOGIT transformation used to create the dependent variable, hence the name Logistic Regression.

- All assumptions of OLS regression are still valid, however deviations are tolerated to a large extent - as end result in most cases require only a rank order.

Logistic Regression produces results in a binary format which is used to predict the outcome of a categorical dependent variable. So the outcome should be discrete/categorical such as:

**Logistic Regression Curve**

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