Linear Models, ANOVA, and Regression in R

Unlock the power of statistical data analysis by mastering linear modeling in R. This comprehensive text-based course guides you from the absolute basics of statistical relationships to building, interpreting, and validating regression and ANOVA models. You will transition from a statistics novice to a confident analyst capable of structuring datasets, running linear models, and extracting meaningful insights using R. What you'll learn: - Understand the core conceptual foundations of linear relationships and statistical hypothesis testing. - Build and interpret simple and multiple linear regression models using standard R syntax. - Perform Analysis of Variance (ANOVA) to compare multiple groups and analyze categorical predictors. - Validate model assumptions such as normality, homoscedasticity, and independence using modern diagnostic techniques. - Apply modern R packages to tidy, summarize, and present statistical model outputs efficiently. - Practice your analytical skills with structured written exercises and step-by-step code walkthroughs. The course begins with essential statistical terminology and foundational definitions before moving into practical R code implementation. You will explore real-world scenarios through structured text explanations and code snippets that demonstrate how to interpret coefficients, evaluate model fit, and check assumptions. This course is designed for beginners, researchers, and aspiring data analysts who want to learn statistical modeling in R, with no prior programming or advanced statistics experience required. Start reading today to build a solid foundation in statistical modeling and data analysis with R.