This module introduces Linear Regression Models, teaching students how to build and estimate regression models using Excel. Students learn to interpret regression output, understand the relationships between variables, and use the models for making predictions. The module covers important concepts like errors, residuals, R-square, and assumptions of normality in regression analysis. Through practical examples, students gain hands-on experience in applying regression techniques to business problems.

This module focuses on hypothesis testing in linear regression and measures of goodness of fit. Students learn to conduct and interpret various hypothesis tests using the regression output, including t-tests and confidence intervals. The module introduces p-values and explains the interpretation of R-square and adjusted R-square as measures of model fit. It also covers the important topic of dummy variable regression, which allows for the inclusion of categorical variables in regression models.

This module expands on the use of dummy variables in regression analysis, covering the extension to multiple categories and the interpretation of coefficients and p-values in the presence of categorical variables. Students apply these concepts to real data examples involving refrigerator and car data. The module also addresses the important issue of multicollinearity in regression models, explaining what it is and how to detect and deal with it to improve model validity.

This final module introduces advanced regression techniques and extensions to enhance model performance and interpretation. Topics include mean-centering variables, building confidence bounds for predictions, interaction effects between variables, and transformation of variables to improve linearity. The module covers specialized regression models such as log-log and semi-log regression, which are particularly useful for certain types of business data. Through practical examples, students learn to apply these advanced techniques to real-world business problems.