Linear Regression for Business Statistics Course Syllabus

Overview: This course provides a practical introduction to linear regression in business contexts, designed for beginners and non-technical professionals. Over approximately 8.5 hours of content, you'll learn to build, interpret, and apply regression models using Microsoft Excel. The curriculum emphasizes real-world applications across business functions such as marketing, finance, and operations. Each module builds foundational analytical skills with hands-on Excel exercises, culminating in a final project that applies regression to solve a business problem. Lifetime access ensures you can learn at your own pace and revisit key concepts whenever needed.

Module 1: Introduction to Regression

Estimated time: 2 hours

• Overview of regression models and their role in business analytics
• Using Excel to estimate regression equations
• Visualizing regression relationships
• Understanding residuals and basic prediction

Module 2: Hypothesis Testing and Model Evaluation

Estimated time: 2 hours

• Hypothesis testing in the context of regression
• Interpreting t-tests and p-values for regression coefficients
• Evaluating model fit using R-squared
• Introduction to dummy variables for categorical data

Module 3: Dummy Variables and Multicollinearity

Estimated time: 2.5 hours

• Building regression models with multiple categorical variables
• Creating and interpreting dummy variables
• Identifying multicollinearity issues
• Understanding the implications of multicollinearity on model interpretation

Module 4: Advanced Topics in Regression

Estimated time: 2 hours

• Working with interaction effects in regression models
• Using centered variables to improve interpretation
• Constructing confidence intervals for predicted values
• Analyzing practical business examples across functions

Module 5: Real-World Applications of Regression

Estimated time: 1.5 hours

• Applying regression to forecasting in business analysis
• Modeling financial behavior and risk assessment
• Assessing marketing campaign effectiveness
• Evaluating HR and operational performance metrics

Module 6: Final Project

Estimated time: 3 hours

• Build a regression model using real business data in Excel
• Interpret coefficients, p-values, and R-squared values
• Submit a report summarizing findings and business implications

Prerequisites

• Familiarity with basic Excel functions
• Basic understanding of business metrics
• No prior statistics or programming experience required

What You'll Be Able to Do After

• Build and estimate linear regression models in Excel
• Interpret regression output including coefficients and p-values
• Use dummy variables to include categorical data in models
• Identify and address multicollinearity and interaction effects
• Apply regression analysis to real-world business decision-making