Regression Modeling and Statistical Analysis with SAS

Data-driven decision-making relies on understanding the relationships within your data. SAS is a powerful tool for statistical analysis, but getting started with regression modeling can feel daunting without a solid foundation. This text-based course guides you through the core concepts of statistical modeling using SAS. You will transition from understanding basic statistical tests to building, validating, and interpreting linear and logistic regression models, giving you the skills to extract actionable insights from raw data. What you'll learn: - Understand the foundational concepts of hypothesis testing, t-tests, and analysis of variance (ANOVA). - Configure SAS procedures to perform simple and multiple linear regression analyses. - Evaluate statistical assumptions, check for multicollinearity, and interpret diagnostic outputs. - Implement basic logistic regression models for binary classification and categorical outcomes. - Apply modern best practices for reporting statistical results and presenting data insights clearly. You will begin with essential terminology and basic statistical tests before advancing to multiple linear regression and introductory logistic regression. Through written explanations and practical SAS code examples, you will learn how to set up analyses and confidently interpret SAS outputs. This course is designed for beginners, data analysts, and researchers who want to learn statistical modeling in SAS. No prior advanced statistical modeling experience is required, as we start with the absolute basics. Start building reliable statistical models and unlocking the power of your data with SAS today.