This course focuses on utilizing Python statsmodels for linear and logistic regression, two widely used statistical models. By mastering these models, you will gain the ability to uncover valuable insights hidden within your data. Throughout the course, you will engage in hands-on exercises that explore the relationships between variables in various real-world datasets, such as motor insurance claims, Taiwan house prices, and fish sizes. The course begins by providing a comprehensive understanding of regression, highlighting the differences between linear and logistic regression, and teaching you how to apply both models effectively. You will also learn how to utilize linear regression models to make accurate predictions and comprehend model objects. As you progress, you will delve into assessing the fit of your models and determining the effectiveness of your linear regression model. Additionally, you will explore logistic regression models in greater detail, enabling you to make predictions using real data. By the end of this 4-hour course, you will have acquired the necessary skills to make predictions, evaluate model performance, and diagnose issues related to model fit. You will have a solid grasp of Python statsmodels for regression analysis and the ability to apply these skills to real-life datasets.