Technically, the intercept in the linear regression model can be positive, negative, or even zero.
Positive Intercept: If the intercept in the regression model is positive, it means that the predicted value of the dependent variable (Y) when the independent variable (X) is zero is positive. line crosses the y-axis above the zero value.
Negative Intercept: Conversely, if the intercept in a italy whatsapp number data linear regression model is negative, it means that the predicted value of Y when X is zero is negative. In this case, the regression line crosses the y-axis below the zero value.
Zero Intercept: If the intercept in a regression model is zero, it implies that the regression line passes through the origin (0,0) on the graph. This means that the predicted value of the dependent variable is zero when all independent variables are also zero. In other words, there is no additional constant term in the regression equation. This situation is extremely rate and very theoretical.
Basically, you deal with negative or positive intercepts, and when you come across the negative intercept you deal with the negative intercept the same way as you would deal with a positive intercept. But in practical terms, a negative intercept may or may not make sense depending on the context of the data being analyzed. For example, if you are analyzing the day’s temperature (X) and sales of ice cream (Y), a negative intercept would not be meaningful since it is impossible to have negative sales. However, in other domains such as financial analysis, a negative intercept could make sense.
Below are some approaches you can consider when you have negative intercepts:
Check for data errors and assumptions: Before making any adjustments, ensure that the regression assumptions are met. This includes linearity, independence, homoscedasticity (pertaining to residuals), normality of the data variables and residuals, outliers, and more. If these assumptions are violated, it is necessary to address them first.