# Regression Example - Creating Model in Data Science

## FAQs

The following are the steps in the process of regression:

• Establishing a set of independent variables that should be included in the regression analysis.
• Establish a set of dependent variables for which you would like to predict values using your predicted values for each independent variable.
• Calculate a predicted value for each dependent variable based on your estimated values for each independent variable and then compare them to actual values to see how well they predict your dependent variables.
• Repeat steps 2 and 3 until you have reached convergence or have reached an endpoint

The equation for a linear regression line is Y = a + bX, with X as the explanatory variable and Y as the dependent variable. The intercept (the value of y when x = 0) is a, while the slope of the line is b.

Regression is a statistical approach for modelling the connection between one or more independent variables and a dependent variable. Regression is one of the most significant methods in Machine Learning and is frequently employed in a variety of statistical analysis tasks.

Regression is a method for modelling and analysing the relationships between variables, as well as how they contribute to and are connected to obtaining a specific outcome. A regression model with only linear variables is referred to as a linear regression.

Regression is a statistical method that allows us to estimate the relationship between two variables. It can also be used to predict an outcome when we have enough information about the model.

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