4. Statistical Analysis with R Programming

Statistical Analysis with R Programming

1. Course Description
The Statistical Analysis with R Programming course is designed to equip learners with strong statistical analysis skills using the R programming language. Participants will learn how to perform data analysis, apply statistical methods, and interpret results effectively. The course combines statistical theory with hands-on R programming, making it ideal for students, researchers, and professionals who work with data-driven decision-making.

2. Course Outlines
Chapter 1: Introduction to Statistics and R Programming
• Overview of Statistics and Its Applications
• Introduction to R and RStudio
• Basic R Syntax and Data Types
Chapter 2: Data Handling and Manipulation in R
• Vectors, Matrices, Data Frames, and Lists
• Importing and Exporting Data
• Data Cleaning and Preparation
Chapter 3: Descriptive Statistics
• Measures of Central Tendency
• Measures of Dispersion
• Data Summarization Techniques
Chapter 4: Data Visualization in R
• Introduction to Data Visualization
• Creating Charts with Base R
• Advanced Visualization Using ggplot2
Chapter 5: Probability and Distributions
• Basic Probability Concepts
• Common Probability Distributions
• Sampling Techniques
Chapter 6: Inferential Statistics
• Hypothesis Testing
• Confidence Intervals
• t-tests and ANOVA
Chapter 7: Regression and Correlation Analysis
• Simple and Multiple Linear Regression
• Correlation Analysis
• Interpreting Statistical Results

3. Who Should Join
• Researchers and analysts
• Students in statistics or economics
• Professionals working with quantitative data