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

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

In this module, we will introduce the basic conceptual framework for statistical modeling in general, and linear statistical models in particular. In this module, we will learn how to fit linear ...

What Are Some Ways Linear Regression Can Be Applied in Business Settings?. During the course of operation, businesses accumulate all kinds of data such as numbers related to sales performance and ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

Antonio Cuevas, Manuel Febrero, Ricardo Fraiman, Linear Functional Regression: The Case of Fixed Design and Functional Response, The Canadian Journal of Statistics / La Revue Canadienne de Statistique ...

Nonlinear regression is a form of regression analysis in which data fit to a model is expressed as a mathematical function.

The 1941–1970 cooling trend is most statistically significant. The primary purpose of utilizing linear regression as a time series method is for visualization of climate change.