In this course, we will learn how linear regression works, how to build effective linear regression models and how to use and interpret the information these models give us. In addition to the theory, we will perform linear regression on real data using both Excel and Python.

This module covers the fundamentals of simple linear regression, including the regression equation, ordinary least squares method, and parameter fitting. It demonstrates practical applications in both Excel (using manual calculations, Data Analysis tools, and the Regressit plugin) and Python (using statsmodels for data analysis, model fitting, and visualization). The module also addresses important considerations when applying regression techniques and includes examples with train-test splits to evaluate model performance.

This module provides a practical assessment of the concepts covered in the Simple Linear Regression module. Students apply their knowledge to solve regression problems and demonstrate their understanding of the basic regression techniques using Excel and/or Python.

This module extends regression analysis to include multiple predictor variables. It covers the theory of multiple linear regression, potential issues like multicollinearity, and demonstrates practical implementation in both Excel and Python. The module shows how to load and assess data, build basic and full regression models using statsmodels, and implement multiple regression using scikit-learn.

This module focuses on how to interpret the outputs of linear regression models. It covers residuals, ordinary least squares assumptions (linearity, normality, heteroscedasticity, zero mean errors, endogeneity, autocorrelation, multicollinearity), and model evaluation metrics (squared error metrics, absolute error metrics, R-squared, adjusted R-squared). The module also explains how to interpret regression coefficients, calculate and understand p-values, analyze different interpretation scenarios, and evaluate residual plots.

This module provides a comprehensive assessment of the concepts covered in the Multiple Linear Regression and Interpreting Linear Regression modules. Students apply their knowledge to solve more complex regression problems, interpret model outputs, and evaluate model assumptions and performance.

This module introduces more advanced regression techniques beyond standard linear regression. Topics include log-log linear regression, polynomial regression, logistic regression, repeated measure regression, and segmented regression models. The module demonstrates how to investigate problems with these models, plot logarithmic transformations, and evaluate model performance for these advanced techniques.

This brief module summarizes the key concepts covered throughout the Regression Analysis course. It recaps the essential topics from simple linear regression, multiple regression, interpretation techniques, and advanced regression models, providing a comprehensive conclusion to the course material.

This final assessment module tests the students' understanding of advanced regression techniques covered in the course. Students apply their knowledge to solve complex regression problems, implement advanced regression models, and interpret their results in the context of real-world data.