Multivariate Regression_ is a method used to measure the degree at which more than one independent variable (predictors) and more than one dependent variable (responses), are linearly related._

Most regression models are described in terms of the way the outcome variable is modeled:

  • In linear regression the outcome is continuous,
  • Logistic regression has a dichotomous outcome,
  • Logistic regression has a dichotomous outcome,

Multivariate analysis refers to statistical models that have 2 or more dependent or outcome variables.

A multivariate linear regression model would have the form

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Y_(n×p)= X_(n×(k+1)) β_((k+1)×p)+ ε

where the relationships between multiple dependent variables (i.e., Ys) — measures of multiple outcomes — and a single set of predictor variables (i.e., Xs) are assessed.

In any data analysis, the goal is to extract the accurate estimation from raw information.

One of the most important question is whether a statistical relationship between a response variable (Y) and explanatory variables (Xi) exists or not.

An option to answer this question is to employ regression analysis in order to model this relationship.

Multivariate Regression is one of the simplest Machine Learning Algorithm.

It comes under the class of Supervised Learning Algorithms i.e., when we are provided with a training dataset.

#data-science #machine-learning #linear-regression #regression

Multiple vs Multivariate Regression
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