Types of Regression Model:
A regression models to predict their future behavior of the past relationship between Variables. As an example, Imagine that your company wants to understand how the past have related to advertising expenditures to sales in order to make decisions about their future. The dependent variable in this instance is the independent variable is the sales and advertising expenditures.Usually, more than one independent variable influences the dependent variable. Imagine that you can in the above example as well as advertising sales are influenced by other factors, such as the number of sales representatives and the percentage commission paid to sales representatives. When one independent variable in a regression is used, it is called a simple regression, when two or more independent Variables are used, it is called a multiple regression.
Either linear or nonlinear regression models can be. Variables are the relationships between the model assumes a linear straight-line relationships, while the model assumes a nonlinear relationships between Variables are represented by the curved lines. In business, you will often see the relationship between the return of an individual stock and the returns of the market, modeled as a linear relationship, while the relationship between the price of an item and the demand for it is often modeled as a nonlinear relationship.
