Multivariate regression models, building upon linear regression, enable the analysis of relationships between multiple independent variables and two or more dependent variables in data analytics.

Learn the steps to interpret a multivariate regression model, and how to apply them to a practical example. Understand the assumptions, parameters, coefficients, fit, and validation of the model.

This project explores the differences between univariate and multivariate linear regression models in the context of predictive performance and interpretability. In a univariate regression, a single ...

The goal of this project is to predict a target variable based on multiple predictors using a multivariate linear regression model. This project demonstrates the process of building, training, and ...

The book has a very broad coverage, from illustrative practical examples in Regression and Analysis of Variance alongside their implementation using R, to providing comprehensive theory of the general ...

9.1.3 Model quality and statistical significance. We will come back to the question of whether the linear model is valid (whether it satisfies the assumptions of the technique). First we want to ...

Simple Linear Regression model. Simple linear regression is a statistical method that enables users to summarise and study relationships between two continuous (quantitative) variables. Linear ...

In contrast to conventional, univariate analysis, various types of multivariate analysis have been applied to functional magnetic resonance imaging (fMRI) data. In this paper, we compare two ...

Econometrics is based on economic data while the data represented by fuzzy sets can not be dealt with classical time series methods. In this paper the author proposes a new kind of variable named ...