Linear Regression Analysis with jamovi

Understanding linear regression is a core skill for anyone working with data, empirical research, or statistical modeling. This text-based guide teaches you how to leverage jamovi, a modern open-source statistical tool, to perform linear regression analysis with confidence and clarity. You will move from fundamental concepts to hands-on statistical workflows, discovering how to prepare your data, set up models, evaluate model assumptions, and translate statistical outputs into meaningful conclusions. What you will learn: - Understand the core principles of correlation, simple linear regression, and multiple linear regression - Navigate the jamovi interface and structure datasets for regression modeling - Check critical regression assumptions including normality, homoscedasticity, linearity, and multicollinearity - Interpret model coefficients, R-squared values, p-values, and confidence intervals accurately - Incorporate categorical predictors and dummy coding into your regression models - Practice standard reporting conventions to document statistical findings clearly The course begins with foundational statistical definitions and terminology before progressing step-by-step through model building, diagnostic checks, and result interpretation. This course is designed for beginners, students, and researchers with no prior advanced statistical background or programming experience required. Start reading today to build practical data analysis skills with jamovi.