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Linear Regression - Lesson Summary

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Linear regression is a linear approach to modelling the relationship between a dependent variable and one or more independent variables.
Simple linear regression is an approach for predicting a quantitative response Y on the basis of a single predictor variable X. It assumes that there is approximately a linear relationship between X and Y. 
In multiple linear regression more than one predictor variables are used to predict the response variable.
The quality of a linear regression fit is typically assessed using two related quantities: the residual standard error (RSE) and the R-squared statistic.
RSE is the average amount that the response will deviate from the true regression line.
Once the model is trained, we can estimate the quality of fit using the Mean Squared Error.
Expected test error = Bias + Variance + Irreducible error
Between Bias and Variance, if we try to decrease one by changing model flexibility, the other one increases.