Regression:

Regression is a statistical method that allows us to model the relationship between a dependent variable and one or more independent variables. The dependent variable is the variable that we are trying to predict, while the independent variables are the variables that we believe influence the dependent variable.


Linier regression:

Linear regression is a statistical method that models the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the variables, and it is typically fit to data using a least squares approach. Once the model is fit, it can be used to make predictions about the dependent variable for new values of the independent variables.

Linear regression is a powerful tool that is used in a wide variety of fields, including finance, marketing, and manufacturing. It is a relatively simple method to understand and implement, and it can be very effective for making predictions.

Here is an example of a simple linear regression model:

y = ax + b

where:

  • y is the dependent variable
  • x is the independent variable
  • a is the slope of the regression line
  • b is the y-intercept of the regression line

The slope of the regression line tells us how much the dependent variable changes for a one-unit change in the independent variable. The y-intercept of the regression line tells us the value of the dependent variable when the independent variable is equal to zero.

Linear regression is a valuable tool for understanding and predicting the relationships between variables. It is used in a wide variety of fields and can be applied to a wide range of problems.

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