✨ Data Analysis Project: Exploring Correlations and Regression Models ✨

Welcome to the Data Analysis Project repository! 🎨 This project involves comprehensive statistical analysis and data visualization using Python. The tasks cover concepts like correlation coefficients, regression models, and hypothesis testing applied across 4 datasets.

---

📁 Project Overview

Datasets:

This repository contains the following datasets:

• ex1.csv
• ex2.csv
• ex3.csv
• ex4.csv

Tasks Completed:

1. Exploring relationships between variables using scatter plots.
2. Calculating correlation coefficients (Pearson and Spearman).
3. Performing hypothesis testing (t-tests, F-tests).
4. Building and analyzing linear and multiple regression models.
5. Visualizing data and results with informative plots.
6. Interpreting statistical significance and model performance.

---

📊 Key Results

Dataset 1: ex1.csv

• Analysis: Scatter plot of two variables and calculation of Pearson correlation.
• Results:
  • Pearson Correlation Coefficient: 0.85 🚀
  • t-statistic: 17.500 ✅ (Significant at 0.05 level).
• Visualization: Scatter plot and a line of best fit.

Dataset 2: ex2.csv

• Analysis:
  • Histograms to check distribution.
  • Box plots to identify outliers.
  • Spearman correlation analysis.
• Results:
  • Spearman Correlation Coefficient: 0.82
  • t-statistic: 15.400 ✅ (Significant).
• Visualization: Histograms, box plots, and scatter plots.

Dataset 3: ex3.csv

• Analysis:
  • Linear regression model fitting.
  • t-test for regression coefficients.
  • Mean absolute error (MAE) calculation.
• Results:
  • Regression Coefficients: Slope = 2.34, Intercept = 0.67
  • F-statistic: 579.421 ✅ (Model significant).
  • MAE: 5.21%
• Visualization: Scatter plots with predicted vs. actual values.

Dataset 4: ex4.csv

• Analysis:
  • Multiple regression analysis.
  • Multicollinearity check.
  • Calculation of regression coefficients and R-squared.
• Results:
  • Adjusted R-squared: 0.94
  • F-statistic: 412.56 ✅ (Model significant).
  • Standardized regression coefficients and confidence intervals.
• Visualization: 3D scatter plots for predicted vs. actual values.

---

🎨 Visualizations

This repository includes the following visualizations for each dataset:

1. Scatter Plots: To visualize relationships between variables.
2. Histograms: To inspect the distribution of variables.
3. Box Plots: To detect and analyze outliers.
4. Regression Plots: To compare actual and predicted values.
5. 3D Scatter Plots: For multiple regression results.

🚀 How to Use

1. Clone the repository:

   git clone https://github.com/AnnaPerova88/MEPHI_Stats_Homework_Module5.git