Model building via linear regression models. Method of least squares, theory and practice. Checking for adequacy of a model, examination of residuals, checking outliers. Practical hand on experience ...

Although [Vitor Fróis] is explaining linear regression because it relates to machine learning, the post and, indeed, the topic have wide applications in many things that we do with electronics and ...

The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an extension of Lesson 9. I will start with a ...

Introduces methods, theory, and applications of linear statistical models, covering topics such as estimation, residual diagnostics, goodness of fit, transformations, and various strategies for ...

The R system has three components: a scripting language, an interactive command shell and a large library of mathematical functions that can be used for data analysis. Although R, and its predecessor ...

Computations that involve matrix algorithms are happening everywhere in the world at every moment in time, whether these be embedded in the training of neural networks in data science, in computer ...

It can be highly beneficial for companies to develop a forecast of the future values of some important metrics, such as demand for its product or variables that describe the economic climate. There ...

During the course of operation, businesses accumulate all kinds of data such as numbers related to sales performance and profit, and information about clients. Companies often seek out employees with ...

We consider the semiparametric linear regression model with censored data and with unknown error distribution. We describe estimation equations of the Buckley-James ...

Although [Vitor Fróis] is explaining linear regression because it relates to machine learning, the post and, indeed, the topic have wide applications in many things that we do with electronics and ...