Statistics In R: Learn To Code In R And Analyze Data

Learn R programming and applied statistics step by step — from data exploration to advanced ANOVA

What you'll learn

Getting Started with R & RStudio

Working with Data

Regression Analyses

Handling Collinearity

Hypothesis Testing

Advanced ANOVA Techniques

Requirements

A computer (Windows, Mac, or Linux).

R and RStudio (installation covered in the first lecture).

No prior programming or advanced math background required—just curiosity and willingness to learn.

Description

Are you ready to transform raw data into meaningful insights using R, one of the most powerful tools for statistical computing and data analysis? Whether you’re a beginner or a professional seeking to sharpen your analytical skills, this course takes you on a step-by-step journey through R programming and applied statistics—from the basics all the way to advanced experimental designs.This practical, hands-on course is designed especially for learners who want to apply statistics in real-world research, healthcare, business, and academic projects. You’ll not only learn the theory but also see how to implement every concept directly in R and RStudio, gaining the skills to analyze, visualize, and interpret data confidently.What You’ll LearnGetting Started with R & RStudioInstall, set up, and navigate R and RStudio with ease. Learn the fundamentals of coding in R even if you’ve never programmed before.Working with DataImport datasets, clean and explore your data, and summarize findings with descriptive statistics and visualizations.Regression AnalysesMaster simple and multiple linear regression, model comparison (hierarchical regression), and predictor selection techniques.Handling CollinearityDetect and deal with collinearity and multicollinearity to improve the reliability of your models.Hypothesis TestingConduct t-tests and one-way ANOVA to compare groups and test statistical hypotheses.Advanced ANOVA TechniquesLearn planned contrasts, factorial ANOVA, repeated measures, and mixed-design ANOVA for more complex experimental designs.By the End of This CourseYou’ll be able to:Import and manage data in R.Generate descriptive and visual summaries.Conduct and interpret regression models.Apply and understand t-tests, ANOVAs, and contrasts.Handle complex experimental designs with confidence.Take this course and unlock the full potential of R for your research and career!

Overview

Section 1: Introduction, Installation, and R Basics

Lecture 1 Introduction and Installation

Lecture 2 Basics of coding in R

Lecture 3 Importing and exploring data

Section 2: Descriptive Statistics & Visualization

Lecture 4 Descriptive statistics and plots

Section 3: Regression Analysis

Lecture 5 Linear regression analyses

Lecture 6 Model comparison (Hierarchical regression)

Lecture 7 Selecting predictors for multiple regression

Lecture 8 Assessing collinearity and multicollinearity

Section 4: Hypothesis Testing & ANOVA

Lecture 9 t-tests and one-way ANOVA

Lecture 10 Planned contrasts

Lecture 11 Factorial ANOVA

Lecture 12 Repeated measures and mixed design ANOVA

Beginners wanting to learn R for data analysis.,Students and professionals in medicine, psychology, social sciences, business, or research.,Anyone preparing to use statistics in academic or applied research.,Learners who want a structured pathway from beginner to advanced statistical methods in R.