https://github.com/balnarendrasapa/education_impact_analysis
This GitHub repository contains the code and documentation for an analysis of the impact of education levels on individuals' performance scores. Using ANOVA, we compare score means across different education groups, revealing significant differences. For a comprehensive analysis and detailed results
https://github.com/balnarendrasapa/education_impact_analysis
anova data-science jupyter-notebook
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This GitHub repository contains the code and documentation for an analysis of the impact of education levels on individuals' performance scores. Using ANOVA, we compare score means across different education groups, revealing significant differences. For a comprehensive analysis and detailed results
- Host: GitHub
- URL: https://github.com/balnarendrasapa/education_impact_analysis
- Owner: balnarendrasapa
- License: apache-2.0
- Created: 2023-09-09T14:26:42.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2023-12-24T00:05:34.000Z (over 2 years ago)
- Last Synced: 2023-12-24T01:21:23.370Z (over 2 years ago)
- Topics: anova, data-science, jupyter-notebook
- Homepage: https://balnarendrasapa.github.io/education_impact_analysis/docs/intro
- Size: 1.21 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Education Level and Performance Analysis
## Background 📚:
This project 📊 analyzes a dataset 📂 containing records of individuals with varying levels of education 🎓, ranging from Less than High School to Graduate, alongside their performance scores 📈. The dataset categorizes people into five different education groups.
## Purpose 🎯:
Our objective 🤔 is to determine whether there is a significant difference in the scores among individuals with different education levels 📝. We aim to answer whether education level has a meaningful impact on a person's performance 🧠. We conducted an analysis 📈 to identify if there is a statistically significant difference in scores among these education groups, ruling out randomness 🎲.
## Methods 📊:
We employed Analysis of Variance (ANOVA) 📊 to compare the means of each education group. This choice was made because we have more than two groups, making the T-Test or Z-Test unsuitable without increasing the type-1 error rate 📉. Our analysis began with Exploratory Data Analysis (EDA) 📊 to prepare the dataset for ANOVA. We formulated hypotheses 📜 for ANOVA, with the null hypothesis stating that there is no difference among the means of the groups and the alternative hypothesis suggesting that at least one group has a different mean.
## Results 📈:
Our analysis yielded a p-value lower than the significance level 📏, and the F-Statistic exceeded the F-Critical value 📊. Consequently, we rejected the null hypothesis in favor of the alternative hypothesis 📉.
## Conclusion 📝:
Based on substantial evidence 📊, we conclude that there is indeed a significant difference among the means of education groups 📚, and this difference is not due to randomness 🎲. Specifically, we found that two pairs of groups have significantly different means: 1) "Less than High School" - "Graduate" and 2) "Less than High School" - "Bachelor's". Therefore, we can assert that individuals who have completed their Graduate or Bachelor's education 🎓 have different scores compared to those who did not pass High School 🏫.
For a more detailed analysis and results, please visit this [website](https://balnarendrasapa.github.io/education_impact_analysis/docs/intro). 🌐