What is the two others name of linear model
Answers
Answer:
Linear models describe a continuous response variable as a function of one or more predictor variables. They can help you understand and predict the behavior of complex systems or analyze experimental, financial, and biological data.
Linear regression is a statistical method used to create a linear model. The model describes the relationship between a dependent variable y (also called the response) as a function of one or more independent variables Xi (called the predictors). The general equation for a linear model is:
y=β0+∑ βiXi+ϵi
where β represents linear parameter estimates to be computed and ϵ represents the error terms.
There are several types of linear regression:
Simple linear regression: models using only one predictor
Multiple linear regression: models using multiple predictors
Multivariate linear regression: models for multiple response variables
Simple linear regression is commonly done in MATLAB. For multiple and multivariate linear regression, see Statistics and Machine Learning Toolbox. It enables stepwise, robust, and multivariate regression to:
Generate predictions
Compare linear model fits
Plot residuals
Evaluate goodness-of-fit
Detect outliers
To create a linear model that fits curves and surfaces to your data, see Curve Fitting Toolbox. To create linear models of dynamic systems from measured input-output data, see System Identification Toolbox. To create a linear model for control system design from a nonlinear Simulink model, see Simulink Control Design.
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Answer:
The general linear model and
the generalized linear model
Explanation:
In statistics , the term linear model is used in different ways according to he context .The most common occurrence in connection with regression models .
The general linear model and the generalized linear model are two commonly used statistical method to relate some number of continuous .
The general linear model is a generalization of multiple linear regression to the case of more than one dependent variable .
And the generalized linear model is a flexible generalization of ordinary linear regression.
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