PYTHON +2.4%EXCEL +3.0%STATISTICS +3.3%ECONOMICS +3.7%FINANCE +4.1%RUNNING STREAK +100%PROBLEM SOLVING +2.9%TINKERING +5.0%DATA VISUAL +3.5%FOCUS +4.2%PYTHON +2.4%EXCEL +3.0%STATISTICS +3.3%ECONOMICS +3.7%FINANCE +4.1%RUNNING STREAK +100%PROBLEM SOLVING +2.9%TINKERING +5.0%DATA VISUAL +3.5%FOCUS +4.2%PYTHON +2.4%EXCEL +3.0%STATISTICS +3.3%ECONOMICS +3.7%FINANCE +4.1%RUNNING STREAK +100%PROBLEM SOLVING +2.9%TINKERING +5.0%DATA VISUAL +3.5%FOCUS +4.2%PYTHON +2.4%EXCEL +3.0%STATISTICS +3.3%ECONOMICS +3.7%FINANCE +4.1%RUNNING STREAK +100%PROBLEM SOLVING +2.9%TINKERING +5.0%DATA VISUAL +3.5%FOCUS +4.2%

ECONOMETRIC ANALYSIS

Utilised advanced statistical methods to analyse consumer behaviour and market trends during economic shifts.

01 — QUANTIFYING BEHAVIOUR

Econometrics is the bridge between theoretical economics and the messy reality of data. This project focuses on isolating the variables that actually drive market shifts, using rigorous statistical testing to move beyond simple correlation.

02 — THE STATISTICAL LENS

Regression Modelling: Implemented multivariate OLS and Probit models to identify the primary drivers of consumer spending in high-inflation environments.

Hypothesis Testing: Conducted robust T-tests and F-tests to validate the significance of economic indicators across diverse demographic datasets.

Data Visualisation: Created complex heatmaps and scatter plots in R (ggplot2) to communicate statistical findings to non-technical stakeholders.

03 — INSIGHT THROUGH DATA

The analysis revealed a 0.88 correlation between local interest rate shifts and mid-market consumer retention, providing a predictive framework with less than 5% error. This project demonstrated the power of disciplined data analysis in making informed economic decisions.

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