Statistics with R Programming - A Practical Guide

Learn essential statistical concepts and techniques through hands-on R programming in this comprehensive video series. Master descriptive statistics for data summarization and visualization, then advance to inferential statistics including hypothesis testing and confidence intervals. Explore regression analysis covering linear, multiple, and logistic regression models, along with ANOVA and categorical data analysis techniques. Develop proficiency with key R packages including tidyverse for data manipulation, ggplot2 for visualization, broom for model output management, infer for hypothesis testing, and gt for professional reporting. Practice conducting t-tests, chi-squared tests, and ANOVA analyses while learning to build and validate regression models, handle variable selection, assess collinearity, and work with effect modifiers and interactions. Gain expertise in model assumptions, Bayesian statistics, and resampling methods essential for modern statistical analysis. Perfect for data analysts, researchers, students, and professionals seeking to apply statistical methods effectively using R programming for data exploration, analysis, and reporting.