### What is Regression?

Predicting a continuous numerical value (ex: wage, selling price, etc.) is Regression.

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Predicting a continuous numerical value (ex: wage, selling price, etc.) is Regression.

Linear models are a class of models in which a response variable is linearly related to one or more predictors.

Linear regression is a statistical technique that relates the mean, or expected value, of a continuous response variable through a weighted combination of one or more independent predictor variables.

Linear regression minimizes the squared difference between the actual values of the response and the predicted values from the model.

Global F-test, R-Squared, MSE, MAE, RMSE, Information Criteria (AIC, BIC)

Some of the assumptions of the linear regression model includes independence, normality, constant variance and linearity

The most common approach to deal with categorical or qualitative predictors is to use dummy encoding to account for their different levels.

If any of the assumptions of linear regression are violated, the model may not be reliable to use for either inference or prediction.

Best approach: In order to find if any of the ‘p’ predictors are helpful in predicting ‘y’, use F-Statistic.

Many non-linear relationships can be transformed into linear relationships through logarithmic and power transformations

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