Hypothesis Testing and Statistical Analysis with SAS

Data is only valuable if you can extract meaningful, statistically sound insights from it. Understanding how to set up, run, and interpret statistical tests is a foundational skill for any data analyst or researcher working with SAS. This written course guides you through the essentials of hypothesis testing using SAS/STAT software. You will transition from understanding core statistical theory to confidently writing SAS programs that execute key analyses, check mathematical assumptions, and output clear results. What you'll learn: - Understand the foundational concepts of hypothesis testing, including null hypotheses, p-values, and significance levels. - Perform and interpret one-sample, paired, and independent t-tests to compare sample means. - Analyze multi-group data using One-Way ANOVA and post-hoc testing to identify significant differences. - Build and evaluate linear regression models to predict continuous outcomes and assess model fit. - Apply logistic regression techniques for binary outcomes and understand odds ratios. - Validate statistical assumptions such as normality and homoscedasticity using SAS diagnostic outputs. You will start with key statistical terminology and basic concepts before moving on to practical SAS code snippets. The material progresses logically from simple group comparisons to multi-variable modeling and assumption validation. This course is designed for beginning data analysts, researchers, and SAS users who want to build a solid foundation in statistical hypothesis testing. No advanced mathematical background is required. Start reading today to unlock the power of statistical modeling in SAS.