Binomial Logistic Regression with Jamovi

Understanding how to analyze and predict binary outcomes is an essential skill across research, business analytics, and social sciences. This text-based guide provides a structured walkthrough of binomial logistic regression using the intuitive and open statistical environment of Jamovi. You will begin with fundamental terminology, probability concepts, and data preparation techniques before progressing into model construction, diagnostics, and result interpretation. By following clear written explanations and structured examples, you will develop the practical skills needed to analyze real-world categorical data independently. What you'll learn: - Understand the core mathematical foundations of log-odds, odds ratios, and binary probability distributions - Prepare and structure datasets with categorical predictors and binary dependent variables - Execute binomial logistic regression analyses accurately within the Jamovi interface - Evaluate model performance using AIC, BIC, pseudo R-squared measures, and classification accuracy tables - Interpret regression coefficients, confidence intervals, and predicted marginal probabilities - Check diagnostic assumptions, assess multicollinearity, and identify influential cases - Communicate statistical findings and summary tables clearly for academic and professional reporting The course begins with foundational concepts and data prerequisites before moving methodically through model specification, diagnostic verification, and interpretation standards. This course is tailored for beginners, students, and research analysts who want a straightforward, accessible introduction to logistic modeling without needing advanced mathematical background. Begin reading today to build practical statistical modeling skills with Jamovi.