Statistical Analysis with Spearman Correlation, Chi-Square Test, and Linear Regression

This repository contains statistical analyses conducted on various datasets related to medical and health factors. The analyses include Spearman correlation, Chi-square test, and Linear Regression to explore relationships and predictive models related to heart attacks and other medical conditions.

📈 Correlation Analysis

Dataset Description: The dataset used for correlation analysis contains characteristics related to heart attacks or factors contributing to heart attacks.

Results: Based on Spearman correlation analysis, the following relationships were observed:

• Strong Positive Correlation: Pressurelow and Pressurehight. Higher Pressurelow correlates strongly with higher Pressurehight.
• Strong Positive Correlation: Troppin and Class. Higher Troppin values correlate strongly with higher Class values.
• Weak Positive Correlation: Age with Pressurehight and Tropponin. Weak negative correlation between Age and Kcm, indicating older age correlates with higher Pressurehight and Tropponin levels, and lower Kcm.
• Weak Positive Correlation: Gender with Pressurehight and Tropponin.
• Weak Positive Correlation: Impulse with Pressurelow.
• Weak Positive Correlation: Kcm with Glucose. Weak negative correlation between Kcm and Troppin.

📊 Chi-Square Test Analysis

Dataset Description: The dataset includes symptoms and patient profiles across various diseases.

Result Interpretations:

1. Fever, Cough, Fatigue: Acceptance of H0 (p-value > 0.05) indicates no significant relationship with Disease.
2. Difficulty Breathing: Rejection of H0 (p-value < 0.05) indicates a significant relationship with Disease.
3. Cough, Difficulty Breathing: Acceptance of H0 (p-value > 0.05) suggests no significant relationship with Diagnosis (Outcome Variable).
4. Fever, Fatigue: Rejection of H0 (p-value < 0.05) indicates a significant relationship with Diagnosis.
5. Age, Gender: Acceptance of H0 (p-value > 0.05) indicates no significant relationship with Disease.
6. Blood Pressure, Cholesterol Level: Acceptance of H0 (p-value > 0.05) indicates no significant relationship with Disease. Rejection of H0 (p-value < 0.05) indicates a significant relationship with Diagnosis.

🚀 Linear Regression

Dataset Description: The dataset consists of features aimed at predicting patients at high risk of heart disease.

Results: The linear regression model demonstrates statistical significance (F-statistic = 17.65, p-value < 0.05) and explains a moderate portion of variance in Heart Disease (R-squared = 53.2%). Independent variables collectively contribute to predicting Heart Disease.

🎯 Conclusion

This repository provides insights into statistical analyses using Spearman correlation, Chi-square test, and Linear Regression methods on medical datasets. These analyses enhance understanding of relationships between medical factors and conditions, facilitating informed decision-making in healthcare contexts.