Training on Mastering Statistical Analysis with R

Module 1: Introduction to R and RStudio

• R as a statistical computing environment
• RStudio IDE: interface and basic functionalities
• Data types and structures in R (vectors, matrices, data frames)
• Basic data manipulation and subsetting
• Case Study: Using R to summarize and analyze organizational performance data
• Practical: Create and manipulate vectors, matrices, and data frames in R

Module 2: Data Import and Export

• Importing data from various formats (CSV, Excel, SPSS, etc.)
• Exporting data to different formats
• Data cleaning and preprocessing
• Case Study: Preparing survey data for analysis using R
• Practical: Import, clean, and export datasets in R

Module 3: Exploratory Data Analysis (EDA)

• Summary statistics (mean, median, mode, standard deviation, etc.)
• Data visualization (histograms, box plots, scatter plots, etc.)
• Correlation and covariance
• Outlier detection
• Case Study: Exploring customer behavior data for business insights
• Practical: Perform EDA and generate visual summaries in R

Module 4: Probability and Distributions

• Probability concepts and rules
• Discrete and continuous probability distributions
• Normal distribution and its properties
• Sampling distributions
• Case Study: Using probability distributions to analyze risk in project outcomes
• Practical: Simulate probability distributions in R

Module 5: Hypothesis Testing

• Hypothesis testing framework
• One-sample and two-sample t-tests
• Chi-square test for independence
• ANOVA (one-way and two-way)
• Case Study: Testing differences in performance across departments
• Practical: Conduct hypothesis tests using real datasets in R

Module 6: Linear Regression

• Simple linear regression
• Multiple linear regression
• Model evaluation (R-squared, adjusted R-squared, F-test)
• Model diagnostics
• Case Study: Predicting sales performance using regression analysis
• Practical: Build and evaluate regression models in R

Module 7: Logistic Regression

• Logistic regression model
• Odds and logit
• Model evaluation (confusion matrix, ROC curve, AUC)
• Case Study: Predicting employee turnover using logistic regression
• Practical: Develop and evaluate a classification model in R

Module 8: Non-parametric Methods

• Rank-based tests (Wilcoxon, Kruskal-Wallis)
• Correlation analysis (Spearman, Kendall)
• Case Study: Comparing customer satisfaction across service groups
• Practical: Apply non-parametric tests in R

Module 9: Advanced Topics in Statistics

• Time series analysis
• Survival analysis
• Bayesian statistics
• Machine learning with R
• Case Study: Forecasting trends using time series data
• Practical: Perform basic time series analysis in R

Module 10: Data Visualization with R

• Advanced data visualization techniques
• Creating interactive plots
• ggplot2 package for advanced visualization
• Case Study: Building a data visualization dashboard for executive reporting
• Practical: Create advanced visualizations using ggplot2 in R