Regression Diagnostics with Jamovi

Accurate statistical modeling requires more than just fitting a line to data points—it demands rigorous validation to ensure conclusions are reliable. This text-based guide teaches you how to identify potential flaws in linear regression models and verify essential statistical assumptions using Jamovi. By following clear written explanations and applied examples, you will develop the practical skills needed to evaluate model quality, identify problematic data points, and choose suitable corrective steps. What you'll learn: - Understand core regression assumptions including linearity, homoscedasticity, and normality of residuals - Inspect residual plots to detect patterns indicating model misspecification - Evaluate multicollinearity using Variance Inflation Factors (VIF) and tolerance metrics - Identify outliers, leverage points, and influential observations with Cook's distance - Apply data transformations and modern robust regression strategies when assumptions are violated - Interpret and report diagnostic findings clearly in research summaries and reports The course begins with fundamental terminology and linear modeling concepts before guiding you through systematic step-by-step diagnostic workflows. Designed for beginners and students, this course requires no advanced statistical background or prior experience with Jamovi. Start reading today to build confidence in validating and reporting regression analyses.