Module 1: Introduction to R and RStudio
- Overview of R and RStudio
- Installing R and RStudio
- Navigating the RStudio Interface
- Basic R Syntax and Operations
- Using R Scripts and R Markdown
- Basic Data Structures in R
- Vectors, Matrices, Lists, Data Frames
- Importing and Exporting Data
- Reading Data from CSV, Excel, and other formats
- Writing Data to Files
Hands-On: Setting up R and RStudio, exploring basic R commands and data structures.
Module 2: Descriptive Statistics
- Measures of Central Tendency
- Mean, Median, Mode
- Measures of Dispersion
- Range, Variance, Standard Deviation, IQR
- Exploratory Data Analysis (EDA)
- Summarizing Data
- Creating Basic Plots (Histogram, Boxplot, Scatterplot)
Skills You Will Learn: Summarizing and visualizing data.
Hands-On: Compute descriptive statistics and create basic visualizations.
Module 3: Probability and Distributions
- Introduction to Probability
- Basic Probability Concepts
- Conditional Probability and Bayes’ Theorem
- Random Variables
- Discrete and Continuous Random Variables
- Probability Distributions
- Binomial, Poisson, Normal, Exponential Distributions
Skills You Will Learn: Understanding probability and probability distributions.
Hands-On: Calculate probabilities, visualize different probability distributions.
Module 4: Sampling and Estimation
- Sampling Techniques
- Simple Random Sampling, Stratified Sampling, Cluster Sampling
- Sampling Distributions
- Central Limit Theorem
- Point Estimation and Interval Estimation
- Confidence Intervals for Means and Proportions
Skills You Will Learn: Sampling methods, calculating confidence intervals.
Hands-On: Perform sampling, calculate and interpret confidence intervals.
Module 5: Hypothesis Testing
- Basics of Hypothesis Testing
- Null and Alternative Hypotheses
- Type I and Type II Errors
- P-values and Significance Levels
- Common Hypothesis Tests
- One-sample and Two-sample T-tests
- Chi-Square Tests
- ANOVA
Skills You Will Learn: Conducting and interpreting hypothesis tests.
Hands-On: Perform various hypothesis tests, interpret results.
Module 6: Regression Analysis
- Simple Linear Regression
- Fitting a Linear Model
- Interpreting Coefficients
- Multiple Linear Regression
- Adding Multiple Predictors
- Checking Model Assumptions
- Model Diagnostics
Skills You Will Learn: Building and interpreting regression models.
Hands-On: Fit and evaluate linear regression models.
Module 7: Advanced Regression Techniques
- Logistic Regression
- Binary Outcomes
- Model Fitting and Interpretation
- Polynomial and Interaction Terms
- Regularization Techniques
- Ridge Regression, Lasso Regression
Skills You Will Learn: Applying advanced regression techniques.
Hands-On: Build and interpret logistic regression models, apply regularization methods.
Module 8: Analysis of Variance (ANOVA) and Experimental Design
- One-Way and Two-Way ANOVA
- Understanding ANOVA Tables
- Post-Hoc Tests
- Experimental Design Principles
- Randomization, Replication, Blocking
Skills You Will Learn: Conducting ANOVA, designing experiments.
Hands-On: Perform ANOVA, design and analyze experiments.
Module 9: Non-Parametric Methods
- Introduction to Non-Parametric Tests
- Mann-Whitney U Test, Wilcoxon Signed-Rank Test
- Kruskal-Wallis Test
- Applications of Non-Parametric Methods
Skills You Will Learn: Conducting and interpreting non-parametric tests.
Hands-On: Perform non-parametric tests, apply them to real-world data.
Module 10: Time Series Analysis
- Introduction to Time Series Data
- Components of Time Series (Trend, Seasonality)
- Time Series Decomposition
- Additive and Multiplicative Models
- Forecasting Techniques
- Moving Averages, Exponential Smoothing
- ARIMA Models
Skills You Will Learn: Analyzing and forecasting time series data.
Hands-On: Decompose and forecast time series data.
Module 11: Final Project and Evaluation
- Capstone Project
- Applying All Learned Concepts
- Data Analysis and Interpretation
- Creating Comprehensive Reports
- Presentation and Feedback
Skills You Will Learn: End-to-end project implementation, presenting insights.
Hands-On: Complete a capstone project involving statistical inference and data analysis.
