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Data Analysis Project

Introduction

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:

  1. Visual Test:

    • Plot error bar graphs and histograms to visually compare the means and confidence intervals for different regions.

  2. 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.

Usage

  1. Run the cells in sequence to perform the analysis step by step.
  2. Visualisations, including error bar graphs and histograms, are saved as image files for reference.
  3. 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.