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mathematical equation for linear regression. This article aims to explain how in reality Linear regression mathematically works when we use a pre-defined function to perform prediction task. Linear Regression representation c onsists of a linear equation that combines a specific set of input values x the solution to which is the predicted output y for that set of input values y.
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Bn Coefficients describing the linear relationship between a combination of the input variables and target variable. Are called the linear regression equations of mathbf Y on mathbf X and the parameters b _ kj are called the regression coefficients see also Regression mathbf X is an observable parameter not necessarily random on which the mean of the resulting function response mathbf Y mathbf X under investigation depends. I need to find two things according to this graph first mathematical equation that represents the data then with linear regression representing the data points as red line and transform it into matrix-vector form.
There are various blogs explaining how to perform linear regression on various datasets.
Lets take a real world example to demonstrate the usage of linear regression and usage of Least Square Method to reduce the errors. These just are the reciprocal of each other so they cancel out. In the above equations y the target variable. The linearity of the learned relationship makes the interpretation very easy.
