Applied Regression Analysis and Forecasting in Excel

Turning raw business data into actionable future predictions is one of the most valuable skills in data analysis. Regression analysis allows you to uncover relationships between variables and forecast trends with statistical confidence. This text-based course guides you through the foundational principles of predictive modeling using Excel. You will transition from understanding basic statistical correlation to building, interpreting, and validating your own regression models. What you'll learn: - Understand the core concepts of correlation, causation, and predictive modeling. - Build simple linear regression models to analyze the relationship between two variables. - Develop multiple regression models using Excel's Analysis ToolPak to evaluate complex scenarios. - Interpret key statistical outputs including R-squared, p-values, and coefficients with confidence. - Apply modern Excel dynamic array formulas like LINEST to generate real-time forecasting models. - Validate model assumptions and identify common pitfalls like multicollinearity or outliers. The course starts with essential statistical definitions and terminology before moving into practical Excel implementations. You will work through written step-by-step explanations and structured exercises to practice interpreting real-world business data. This course is designed for business analysts, students, and aspiring data professionals who are new to statistical modeling. No advanced mathematical background or prior programming experience is required. Start reading today to unlock the predictive power of your data using Excel.