I am often asked about the role of intercepts in linear regression models – especially the negative intercepts. Here is my blog post on that topic in simple words with minimal statistical terms.
Regression models are used to make predictions. Thecoefficients in the equation define the relationship between each independent variable and the dependent variable. The intercept or constant in italy whatsapp number data the regression model represents the mean value of the response variable when all the predictor variables in the model are equal to zero. In linear regression, the intercept is the value of the dependent variable, i.e., Y when all values are independent variables, and Xs are zero. If X sometimes equals 0, the intercept is simply the expected value of Y at that value. Mathematically and pictorially, a simple linear regression (SLR) model is shown below.
But what is the business interpretation of intercept in the regression model? In business terms, an intercept represents a baseline or starting point for the dependent variable, if the independent variables are set to zero. The intercept serves as the starting point for evaluating the effects of the independent variables on the dependent variable. It reflects the portion of the dependent variable that is not influenced by the independent variables included in the model. It helps quantify the impact of changes in the independent variables from this baseline value. For example, in a sales prediction model, the intercept might represent the expected sales when all marketing efforts, i.e., the predictors are at zero. In finance, the intercept can represent fixed or overhead costs that are incurred regardless of the level of activity or other factors.