Regression Analysis for Statistics & Machine Learning in R

This course delves into regression analysis using R, covering key concepts, software tools, and differences between statistical analysis and machine learning. - You'll learn data reading, cleaning, exploratory data analysis, and ordinary least squares (OLS) regression modeling, including theory, implementation, and result interpretation. - You'll tackle multicollinearity with techniques like principal component regression and LASSO regression, and cover variable and model selection for performance evaluation. - You'll handle OLS violations through data transformations and robust regression, and explore generalized linear models (GLMs) for logistic regression and count data analysis. - Advanced sections include non-linear and non-parametric techniques such as polynomial regression, GAMs, regression trees, and random forests. Ideal for statisticians, data analysts, and machine learning practitioners with basic R knowledge, this course blends theory with hands-on practice to enhance your regression analysis skills.