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Data Modelling Course Materials

This repository contains code and analysis performed for my DATA MODELING graduate course at Harvard University. The files are presented in both R Markdown (.rmd) and PDF formats for comprehensive documentation and readability.

Repository Structure

•	/code/: Contains R Markdown files (.rmd) used for various data modeling assignments and projects.
•	/output/: Contains the rendered PDF versions of the .rmd files for easy viewing of code and results.
•	/data/: Contains the data that was provided outside of the existing libraries mentioned in the .rmd files.

Course Overview

The course focuses on:

•	Building and analyzing statistical models using R.
•	Applying techniques such as regression, ANOVA, and data visualization.
•	Understanding and interpreting model diagnostics and results.

Contents

Key Topics Covered:

•	Linear Regression Analysis: Modeling relationships between variables.
•	ANOVA: Analysis of variance for hypothesis testing.
•	Residual Analysis: Evaluating model fit through residuals and plots.
•	Regularization Techniques: Ridge, Lasso, and Elastic Net.
•	Exploratory Data Analysis (EDA): Visual and statistical exploration of data.

Example Files:

•	linear_model_analysis.rmd & linear_model_analysis.pdf: Code and report for linear model building.
•	anova_project.rmd & anova_project.pdf: Analysis of variance project and findings.

Prerequisites

To run the .rmd files locally, ensure you have: • R installed (version X.X or higher) • RStudio for easy editing and rendering of .rmd files • Required R packages: ggplot2, dplyr, tidyverse, etc.

How to Use

  1. Clone the repository:

    git clone https://github.com/break/datamod.git
  2. Open and run .rmd files in RStudio to reproduce the analysis.

  3. View PDF reports for a snapshot of the analysis and results.

License

This repository is for educational purposes related to coursework at Harvard University. Feel free to use the code as a reference but ensure proper attribution for any direct use.

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Amalgamation of Assignments for Hakan Gogtas 2024 Fall Semester Class

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