Statistical Inference and Modelling with R

Understanding data requires more than just running software; you need to know why statistical methods work and when to apply them. This course helps you bridge the gap between statistical theory and practical implementation using R. You will transition from a beginner to a confident data analyst capable of selecting, building, and interpreting statistical models, learning to recognize when standard models fit your data and how to pivot when your statistical assumptions are violated. What you'll learn: - Understand core concepts of probability, hypothesis testing, and confidence intervals. - Build and evaluate linear and logistic regression models using R. - Apply modern tidyverse workflows to clean and prepare data for statistical analysis. - Assess model assumptions and identify alternative methods when those assumptions fail. - Interpret statistical outputs to make valid, data-driven decisions and predictions. The course begins with essential statistical terminology and foundational probability concepts before moving into hands-on modeling. You will progress from basic descriptive analysis to practical regression techniques, learning how to write clean, reproducible R code along the way. Designed for beginners, this text-based course requires no prior background in statistics or programming. Start reading today to unlock the power of statistical modeling with R.