•a statistical method used in the field of data analysis and predictive modeling. It's particularly useful in understanding and modeling relationships between a dependent variable (target) and one or more independent variables (features or predictors).

•is the variable that you are trying to understand, predict, or explain in a statistical analysis or experiment.

It's the variable that you measure or observe. Its value depends on the values of other variables in the study

," is the variable that you manipulate or examine to see how it affects the dependent variable.

•It involves one independent variable, and the equation takes the form y = mx + b, where 'm' is the slope and 'b' is the intercept.

It involves multiple independent variables, and the equation becomes y = b0 + b1x1 + b2x2 + ... + bnxn, where 'b0' is the intercept, and 'b1, b2, ... bn' are the coefficients for the respective independent variables.

Assumptions of Simple Linear Regression

The relationship between the variables is linear

The residuals (errors)

•: The variance of the residuals is constant.

: The residuals are normally distributed.