Analysis of Educational Impact on Math Scores

Overview

This repository contains a series of analyses to investigate the impact of parental education level on students' math scores. The analysis includes statistical tests and correlation studies to determine whether there's a significant difference in math scores based on whether students' parents attained higher education or not.

Contents

data_parents_higher: Dataset containing math scores of students whose parents attained higher education.

data_parents_nothigher: Dataset containing math scores of students whose parents did not attain higher education.

Key Analyses

1. Pearson Correlation Coefficient: Calculated the Pearson correlation coefficient between math scores and writing scores to determine the strength of their linear relationship.

2. Permutation Test: Conducted a permutation test to assess whether the observed difference in mean math scores between students with higher-educated parents and those without is statistically significant.

3. Difference in Means: Computed the difference in mean math scores between the two groups and tested the significance of this difference using permutation testing.

Results

Pearson Correlation Coefficient: A strong positive correlation of approximately 0.80 was observed between math scores and writing scores.

p-Value: A p-value of 0.0 indicates a highly significant correlation, suggesting that the observed correlation is unlikely to have occurred by random chance.

Difference in Means: The analysis revealed a notable difference in mean math scores between the two groups, with statistical significance confirmed through permutation testing.

Usage

To reproduce the analysis or apply it to your own datasets, follow these steps:

Clone the repository: git clone https://github.com/yourusername/educational-impact-analysis.git

Navigate to the project directory: cd educational-impact-analysis

Data source: [https://www.kaggle.com/datasets/spscientist/students-performance-in-exams]