In this course, I will teach you one of the most commonly used classification techniques in data science, machine learning and statistics and that is: Binary Logistic Regression.

A binomial logistic regression is used to predict the binary output (yes/no, true/false, sick/healthy) based on one or more continuous independent variables. It is often referred to as logistic regression. However, in Minitab, it is called binary logistic regression.

I will use Minitab 19 to perform the analysis. The focus of my teaching will be on explaining the theoretical concepts and on analyzing and interpreting the results of the analysis.

Performing Binary Logistic Regression in Minitab is easy. It is just a few selections and clicks and you are done with it. However, the difficult part is understanding and interpreting the results.

The following concepts are covered in this course:

• The purpose of Binary Logistic Regression

• The concept of Odds and ln(Odds)

• Logistic Regression Equation

• Odds Ratio

• Confusion Matrix

• Receiver Operating Characteristic (ROC) Curve

• R-Squared in context of Logistic Regression

• Hosmer-Lemeshow Goodness of Fit Test

• Project work to practice all the above concepts

In the project work, marketers at a cereal company investigate the effectiveness of an ad campaign for a new cereal. Three factors considered in this project are the income, whether the person has seen the advertisement and whether the person has kids in the house.

Here we performed binary logistic regression to determine whether people who saw the ad are more likely to buy the cereal.

After completing this course, you will be easily able to perform the logistic regression, select the model.