Business Analytics with R: Advanced Regression Modeling

To make strategic business decisions, organizations rely on data-driven models that go beyond simple correlations. Understanding how multiple variables interact and predict outcomes is essential for modern business analysts and managers. This text-only course guides you through advanced regression-type statistical models using the R programming language. You will learn to analyze complex business datasets, evaluate model performance, and extract actionable insights for management decisions, all through clear written explanations and structured code walkthroughs. What you'll learn: - Understand the foundational theory behind multiple linear and logistic regression models - Apply advanced regression techniques to real-world business and management scenarios - Implement modern R workflows using tidyverse and tidymodels for data preparation and modeling - Interpret statistical outputs, coefficients, and p-values to make informed business recommendations - Evaluate model fit, address multicollinearity, and validate predictive performance - Practice diagnostic testing to ensure your statistical models are robust and reliable The course begins with essential statistical terminology and foundational regression concepts before moving into practical modeling. You will progress from basic multiple regression to advanced generalized linear models, learning how to interpret and communicate your findings effectively. This course is designed for beginners in business analytics, managers, and aspiring data analysts. No advanced mathematical background or prior R programming experience is required. Start reading today to unlock the power of predictive business modeling with R.