The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate ...

Businesspeople need to demand more from machine learning so they can connect data scientists’ work to relevant action. This requires basic machine learning literacy — what kinds of problems can ...

Andriy Blokhin has 5+ years of professional experience in public accounting, personal investing, and as a senior auditor with Ernst & Young. Thomas J Catalano is a CFP and Registered Investment ...

In a recent write-up, [David Delony] explains how he built a Wolfram Mathematica-like engine with Python. Core to the system is SymPy for symbolic math support. [David] said being able to work ...

The goal of a time series regression problem is best explained by a concrete example. Suppose you own an airline company and you want to predict the number of passengers you'll have next month based ...

The data doctor continues his exploration of Python-based machine learning techniques, explaining binary classification using logistic regression, which he likes for its simplicity. The goal of a ...

The paper studies estimation of partially linear hazard regression models with varying coefficients for multivariate survival data. A profile pseudo-partial-likelihood estimation method is proposed.