Why Andrew Benintendi's regression and ongoing slump is a major problem for Red Sox Benintendi is supposed to be a long-term building block ...

In this article, a Bayesian model for a constrained linear regression problem is studied. The constraints arise naturally in the context of predicting the new crop of apples for the year ahead. We ...

Using historical data and regression analysis has its limitations in business forecasting. For example, a significant correlation between the independent and dependent variable does not ...

One useful tool to help us make sense of these kinds of problems is regression. Regression is a statistical method that allows us to look at the relationship between two variables, while holding other ...

Learn the difference between linear regression and multiple regression and how investors can use these types of statistical analysis.

The problem of selecting the best subset or subsets of independent variables in a multiple linear regression analysis is two-fold. The first, and most important problem is the development of criterion ...

Multivariable analysis is a widely used statistical methodology for investigating associations amongst clinical variables. However, the problems of collinearity and multicollinearity, which can ...