ntl-gdp-regression-analysis

Compare nighttime light and GDP relationships across multiple years using automated multi-model regression. This fits Linear, Log-Linear, Quadratic, Power, and Exponential models, then uses R², RMSE, AIC, and BIC diagnostics to select the best fit for your GDP indicator.

Compare regression models for nighttime light economic analysis by evaluating R², RMSE, AIC, and BIC metrics. This automated suite tests five model types against multi-year ANTL and GDP data to select the optimal fit with clear rationale and visualizations.

Use pandas and scikit-learn to validate GDP predictions against official statistics by fitting ANTL data across 5–10 year windows. This generates regression parameters, predictions, and visualizations for decision-ready regional GDP insights.

This regression workflow supports multi-year regional studies like Shanghai GDP estimation by retrieving ANTL and GDP data across years. It fits multiple model types, computes diagnostics, selects the best model, and generates plots and summaries for indicator reporting.

Generate visualizations and summaries after running GDP-NTL regression diagnostics by using the built-in reporting outputs. The workflow automatically produces regression parameters, predictions, and plots for decision-ready insights once the best model is selected.