The Unofficial Google Data Science Blog

DECEMBER 4, 2019

With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. It provides the occasion for deeper exploration of which inputs that can be influenced and which risks can be proactively managed. The alternative we use is the forecast triangulation framework described above.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

CIO Business Intelligence

NOVEMBER 29, 2023

When CEO Plinio Ayala joined Per Scholas in 2003, he noticed there weren’t enough skilled technicians to fix the hardware the organization collected. It was just talking about how computers work and the theory of code and the theory of statistical analysis and how best to write your code,” says Wilson.

IT 98

IT Software Technology Cost-Benefit 98

bridgei2i

MAY 26, 2020

It is even more essential now that supply chains are empowered with a high standard of data and analytics sophistication to be able to cost-effectively serve the company’s purpose and combat risks at the same time. You know, Chief Risk Officers, for example, will no longer be confined to the credit industry. Anushruti: Perfect.

Enterprise 131

Enterprise Management Forecasting Manufacturing 131

O'Reilly on Data

DECEMBER 9, 2019

We develop an ordinary least squares (OLS) linear regression model of equity returns using Statsmodels, a Python statistical package, to illustrate these three error types. CI theory was developed around 1937 by Jerzy Neyman, a mathematician and one of the principal architects of modern statistics.

Statistics 27

Statistics Uncertainty Risk Marketing 27