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A correlation analysis determines the strength and direction of a linear relationship between two variables, whereas a simple linear regression analysis calculates parameters in a linear equation that may be used to forecast the values of one variable based on the values of the other. Correlation.

Only one independent variable is present in simple linear regression, and the model must identify a linear relationship between it and the dependent variable. Multiple Linear Regression, on the other hand, uses more than one independent variable to find a relationship.

To model the relationship between two continuous variables, simple linear regression is utilised. The goal is frequently to anticipate the value of an output variable (or responder) based on the value of an input variable (or predictor).

In a nutshell, linear regression is a useful supervised machine learning approach for modelling linear connections between two variables. Simple linear regression is a nice place to start when looking at our data and considering how to develop more complex models.

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