Explain linear regression

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Accepted Answer

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.

Linear regression is used to predict the value of a dependent variable based on the value of one or more independent variables.
It assumes a linear relationship between the independent and dependent variables.
The goal of linear regression is to find the best-fitting line that minimizes the sum of squared differences between the observed values and the values predicted by the model.
The equation for a simple linear regression model is y = mx + b, where y is the dependent variable, x is the independent variable, m is the slope of the line, and b is the y-intercept.
Example: Predicting house prices based on square footage, number of bedrooms, and location using linear regression.

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