What is linear regression and how does it work?

What is linear regression and how does it work?

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship, meaning that changes in the independent variable(s) will result in proportional changes in the dependent variable.

Can you give me an example of linear regression in real life?

Sure! A common example is predicting house prices based on features like size, number of bedrooms, and location. By using linear regression, we can find a line that best fits the data points of past sales to predict future prices.

What are the assumptions behind linear regression?

The main assumptions are: linearity, independence of errors, homoscedasticity (constant variance of errors), and normality of error terms. These ensure the model's validity.

How do I evaluate the performance of a linear regression model?

You can evaluate it using metrics like R-squared, which indicates how well the independent variables explain the variability of the dependent variable, and Mean Squared Error (MSE), which measures the average squared difference between predicted and actual values.