Statistical Analysis and Modeling in R

Accurate data interpretation is the backbone of research and business intelligence, but mastering the tools for analysis can be challenging. This course provides a clear, step-by-step approach to performing statistical tests and building models using the R language. You will progress from understanding basic data distributions to implementing complex regression models, gaining the confidence to choose the right statistical test for any dataset. What you'll learn: - Understand core statistical terminology and the R programming environment - Apply descriptive statistics to summarize and explore new datasets - Perform hypothesis tests including t-tests, ANOVA, and correlation analysis - Build and interpret multiple linear and logistic regression models - Master modern data workflows using Tidyverse for efficient analysis - Practice statistical problem-solving through written code examples and exercises The curriculum begins with foundational definitions and environment setup, ensuring you have a solid grasp of the basics before moving into inferential statistics and predictive modeling. This course is designed for beginners and professionals in quantitative fields who are new to statistical programming. No prior experience with R or advanced statistics is required. Start building your statistical expertise with R today.