Open-source tools have made MMM more accessible, but reliable results still depend on clean data, thoughtful modeling, and ...

Uncover the hidden pitfalls of Excel regression and learn why Python is the key to unlocking clean, efficient data analysis.

We study inference via heteroskedasticity in linear models commonly used for macroeconomic policy analysis, where covariate endogeneity must often be addressed with limited time and data. Our ...

Since version VM 7.1.0 you can access the Calcpad math engine from Python via the new PyCalcpad.dll API library. It is wrapped on its turn by PyCalcpadWrapper.py that allows you to call all methods ...

The era of size inclusivity is seemingly over. Our critic traces the shift and hopes designers might learn from it. By Vanessa Friedman I know models have always been skinny, but it seems to me they ...

An AI model that learns without human input—by posing interesting queries for itself—might point the way to superintelligence. Save this story Save this story Even the smartest artificial intelligence ...

The Tesla Model Y’s midcycle refresh brought significant enough changes to earn it a spot in our 2026 SUV of the Year competition. The full list of updates is extensive, but the highlights matter.

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

In forecasting economic time series, statistical models often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining ...