Multiple hypothesis testing is an important part of many high-throughput data analysis workflows. A common objective is to maximize the number of discoveries while controlling the expected fraction of ...

A semi-parametric generalization of the proportional hazards regression model is defined, whereby the hazard functions can cross for different values of the covariates. In the two-sample comparison, ...

This calculation can be used for hypothesis testing in statistics Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive ...

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate correlation coefficient. If you look at the multiple regression we did, ...

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 ...

Last month we explored how to model a simple relationship between two variables, such as the dependence of weight on height 1. In the more realistic scenario of dependence on several variables, we can ...

The goal of a machine learning regression problem is to predict a single numeric value. For example, you might want to predict an employee's salary based on age, height, years of experience, and so on ...