Day 1:  Statistical Analysis

• Descriptive Statistics

  • Mean, median, mode, variance, standard deviation

  • Frequency tables and cross-tabulations

• Inferential Statistics

  • Hypothesis testing: t-tests, chi-square tests

  • Correlation and regression analysis

• Case Study: Performing Statistical Analysis

  • Applying statistical methods to real-world data

• Practice: Conduct descriptive and inferential statistics on a dataset

Day 2: Linear Regression in R

• Introduction to Linear Regression

  • Understanding the concept of linear regression

  •  The linear regression model equation: \( Y = \beta_0 + \beta_1X + \epsilon \)

  • Assumptions of linear regression

• Implementing Linear Regression in R

  • Using the `lm()` function to fit a linear model

  • Interpreting model output: coefficients, R-squared, p-values

  • Predicting values using the model

• Model Diagnostics

  • Checking residuals for normality and homoscedasticity

  • Detecting multicollinearity using Variance Inflation Factor (VIF)

  • Assessing model fit and potential overfitting

• Visualizing Linear Regression

  • Plotting the regression line on a scatter plot

  • Residual plots to diagnose model issues

  • Using `ggplot2` for advanced visualization

• Case Study: Real-World Data Application

  • Fitting a linear regression model to a real dataset

  • Interpreting results and drawing conclusions

  • Presenting findings through visualizations and summaries

• Practice: Conducting a linear regression analysis on a dataset

Day 3: Project Day and Best Practices

• Project: Analyzing a Real Dataset

  • End-to-end analysis of a dataset

  • Importing data, cleaning, analysis, and visualization

  • Presenting findings in a report or markdown document

• Best Practices in R Programming

  • Writing clean and efficient code

  • Documenting your work

  • Version control with Git and RStudio

• Q&A and Wrap-Up

  • Addressing questions and challenges

  • Resources for further learning