This Jupyter Notebook university coursework project focuses on analysing 2 types of data, health-related data for the state of Florida and wine quality data. The project uses the following libraries: matplotlib, numpy (imported by %pylab), pandas, scipy, and statsmodels.api.
Methods:
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Visual Test:
- Plot error bar graphs and histograms to visually compare the means and confidence intervals for different regions.
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Statistical Calculations:
- Perform normality tests and Mann-Whitney U tests to compare the datasets statistically.
- Provide a concise conclusion for each comparison, including information on normality tests, t-tests, and Mann-Whitney U tests.
- Run the cells in sequence to perform the analysis step by step.
- Visualisations, including error bar graphs and histograms, are saved as image files for reference.
- The statistical conclusion for each comparison is provided based on the analysis performed in CELL3.
Note: Ensure the '2017Health.txt' file is present in the working directory for proper execution.
Note: The conclusions and visualisations provided in this project are based on the specific indicators mentioned in the dataset and may not cover a comprehensive analysis of overall data samples.