Module 3: Numerical Analysis

This final assessment uses a diabetes dataset to investigate factors associated with diabetes outcomes. The revised R analysis includes data preparation, exploratory visualisation, association tests, logistic regression, a decision tree and ten-fold cross-validation. The accompanying presentation will be linked here when it is uploaded.

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Units 1–4 established my R workflow through data import, descriptive summaries and visualisation. I worked with survey and Indian COVID-19 data, created histograms, density, box and violin plots, recoded case categories, and explored probability through the birthday problem.

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Seminar submissions in Units 6 and 7, followed by Unit 8 activities, developed practical analytical skills. I used density, box, strip and jitter plots and applied ANOVA, Tukey, t-test, Wilcoxon and variance checks to investigate differences between groups.

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Unit 9 used Crime Survey for England and Wales data to explore perceived safety across groups. I created comparative charts, worked with contingency tables and proportions, and applied chi-square testing to investigate associations.

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A second Unit 9 investigation returned to Indian COVID-19 records. I grouped cases by state and severity, compared recovery patterns, and joined the data to a state boundary file to map the distribution of records.

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Units 10 and 11 progressed to normality checks, Pearson and Spearman correlation, linear regression, confidence intervals, residual diagnostics and model interpretation using health and COVID-19 data.

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