This project performs statistical analysis on healthcare data to explore relationships between patient attributes such as age, blood pressure, cholesterol levels, and recovery status. It demonstrates how basic statistical techniques can be applied to healthcare datasets using Python.
The dataset includes 50 patient records with the following columns:
- Patient_ID
- Age
- Blood_Pressure
- Cholesterol
- Recovery_Status (Recovered / Not Recovered)
This project applies various statistical tests and visualizations to derive insights from the data.
- Descriptive Statistics (Mean, Median, Standard Deviation)
- Correlation Analysis
- T-Test (Independent Samples)
- Chi-Square Test
- Data Visualization with Matplotlib
- Python 3
- Pandas
- NumPy
- Matplotlib
- SciPy
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Statistical summary of healthcare parameters
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Correlation between blood pressure, cholesterol, and recovery status
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Chi-square results showing relationships between categorical variables
This project helps understand:
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How to apply statistics in healthcare data
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Real-world use of t-test and chi-square test
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Building insights from small medical datasets