How does linear regression work?

Question

Accepted Answer

Linear regression models the relationship between a dependent variable and one or more independent variables using a linear equation.

It predicts outcomes by fitting a line to the data points.
The equation of the line is typically written as Y = a + bX, where Y is the dependent variable, X is the independent variable, a is the intercept, and b is the slope.
The slope (b) indicates how much Y changes for a one-unit change in X.
For example, in predicting house prices, X could be the size of the house, and Y would be the price.
Linear regression can be simple (one independent variable) or multiple (multiple independent variables).

Accepted Answer

Linear regression is a method of fitting a regression line to data and getting predictions for new values. Linear regression is based on the hypothesis that the dependent variable has a linear relation with independent variables. The functional form linear regression is y=mx+c+e,

where y is the dependent variable, x is the independent variable, m and c are the parameters of the model and e is the error term.

The cost or loss function in linear regression is OLS (ordinary least square).

The basic assumptions of linear regression are:

There is a linear relation between dependent and independent variables.
The error values in prediction are normally distributed.
The error values in prediction have constant variance (Homoscedascity)
The error values in prediction are not autocorrelated.
The independent variables do not have multi-collinearity.

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How does linear regression work?

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