Regression Analysis for Data Science and Decisions

Every business and scientific field relies on data to predict the future, but raw numbers are meaningless without the right analytical tools. Understanding how variables relate to one another is the key to unlocking actionable insights from your datasets. This text-based course guides you from statistical basics to building and interpreting regression models. You will gain the practical skills needed to analyze trends, evaluate model strength, and make accurate, data-backed predictions in your daily work. What you'll learn: - Understand foundational statistical concepts, including correlation, variance, and the mechanics of ordinary least squares. - Build simple linear regression models to analyze the relationship between two variables. - Extend your skills to multiple regression to account for multiple influencing factors simultaneously. - Evaluate model performance using key metrics like R-squared, Adjusted R-squared, and residual analysis. - Identify and resolve common modeling issues such as multicollinearity, overfitting, and non-linear relationships. - Interpret regression coefficients to explain the real-world impact of your findings to stakeholders. You will start with core terminology and mathematical foundations before moving step-by-step through practical modeling scenarios, complete with written explanations and dataset examples to solidify your understanding. Designed entirely for beginners, this course requires no prior background in advanced statistics or programming. Start reading today to master the core predictive tool of data analysis.