Intermediate Statistical Analysis in R

Moving beyond basic data entry and simple descriptive statistics is crucial for making data-driven decisions. This course bridges the gap by teaching you how to perform and interpret intermediate statistical tests using R. Through clear, written explanations and practical code examples, you will transition from a basic R user to a confident data analyst capable of running complex statistical models. You will learn not just how to execute commands, but how to verify assumptions, choose the correct test, and accurately interpret the statistical output for real-world applications. What you'll learn: - Apply parametric and non-parametric association tests, including Pearson, Spearman, and Chi-Square - Conduct group comparison tests such as t-tests and factorial ANOVA while verifying their underlying assumptions - Build and evaluate multiple linear regression models to predict outcomes and analyze relationships - Implement modern tidy data workflows to clean and structure statistical model outputs efficiently - Measure scale reliability and internal consistency using metrics like Cronbach's alpha The course begins with foundational concepts of statistical hypothesis testing before guiding you step-by-step through mean comparisons, correlation analyses, regression modeling, and reliability metrics. Each module combines theoretical context with clean, executable R code to reinforce your understanding. This course is designed for aspiring data analysts, researchers, and students who have a basic familiarity with R and want to expand their statistical toolkit. Start mastering intermediate statistical methods in R today.