Logistic regression is a statistical method used to describe the relationship between one binary dependent variable and various independent variables. It includes different types like binary, multinomial, and ordinal logistic regression, each suited for distinct kinds of data outcomes. Key assumptions for effective logistic regression include sufficient sample size, absence of multicollinearity, and no outliers.

1. Logistic Regression
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2. What is Logistic Regression?
Logistic regression is a statistical technique for describing and
explaining the connection between one dependent binary
variable and one or more nominal, ordinal, interval, or ratio-
level independent variables.

3. Adequate sample size (too few participants for too
many predictors is bad).
Absence of multicollinearity (multicollinearity =
high intercorrelations among the predictors).
No outliers
Assumptions of Logistic Regression

4. Binary Logistic Regression
Multinomial Logistic Regression
Ordinal Logistic Regression
Types of Logistic Regression

5. Based on the values of the independent
variables, binary logistic regression is used to
estimate the likelihood of being a case
(predictors).
The odds are calculated by dividing the chance
that a given result is a case by the probability
that it is not.
Binary Logistic Regression

6. Multinomial logistic regression is a classification
technique that extends logistic regression to
situations with more than two discrete outcomes.
Three or more categories without ordering.
Example: Predicting which food is preferred more
(Veg, Non-Veg, Vegan)
Multinomial Logistic Regression

7. Ordinal Regression (sometimes called Ordinal
Logistic Regression) is a binomial logistic
regression extension.
With ‘ordered' multiple categories and
independent variables, ordinal regression is used
to predict the dependent variable.
Ordinal Logistic Regression

8. Topics for next Post
Naive bayes
Linear Discriminant Analysis
Decision tree
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