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Linear Regression in R using Partial Least Squared Regression

Linear regression is a statistical method that helps to understand the relationship between a dependent variable and one or more independent variables. It is represented by an equation in the form of Y = a + bX, where Y is the dependent variable, X is the independent variable, a is the y-intercept, and b is the slope of the line. It helps to make predictions about the dependent variable, based on the values of the independent variables.

Partial Least Squared Regression (PLSR) is a variation of linear regression that uses a technique called partial least squares (PLS) to reduce the number of independent variables. PLSR is particularly useful when the number of independent variables is large, and when there is a high degree of correlation between the independent variables, known as multicollinearity.

PLS works by projecting the independent variables onto a new set of variables, called latent variables, that are chosen to explain the maximum variance in the dependent variable. These latent variables are then used as independent variables in linear regression, instead of the original independent variables.

PLSR is a two-step process, first, it performs PLS on the independent variables to obtain latent variables. Second, it performs linear regression using the latent variables as independent variables.

It’s important to note that PLSR is not always better than the regular linear regression, it depends on the data and the problem at hand. Also, PLSR assumes that the relationship between the independent and dependent variables is linear. If the relationship is non-linear, PLSR may not provide accurate results.

In this Data Science Recipe, you will learn: Linear Regression in R using Partial Least Squared Regression.

Linear Regression in R using Partial Least Squared Regression

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Linear Regression in R – partial least squares regression in R

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