# Linear Regression

### What is Regression?

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

### Differentiate between Linear Models and Non Linear Models

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

### What is Linear Regression?

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.

### How are coefficients of linear regression estimated?

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

### What are some of the evaluation criteria used to assess the fit of a Linear Regression model?

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

### What are the assumptions of linear regression?

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

### How can qualitative predictors be incorporated in linear regression?

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

### What are potential problems encountered in Linear Regression?

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

### Suppose there are a large number of predictors ‘p’. What is the best approach to find out if any of the p predictors are helpful in predicting the response ‘y’?

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