Multiple regression is a statistical tool used to analyze the relationship between more than two variables.

Multiple regression can be modeled into a simple equation that estimates the linear relationship between the response or dependent variable, with more than one predictor or independent variables.

For example, the water consumption of athletes is positively correlated with both the temperature and the total amount of time practiced.

Here, the temperature and the total amount of time practiced are the predictor variables that can be independently set. The water consumption is the response variable as it depends on the other two variables.  

Since the manual calculation of the multiple regression equation is generally complex, software is used to solve it. 

The multiple coefficient of determination is calculated to measure how well the equation fits the data set. It means that the changes in temperature and the total amount of time practiced can explain 97% of the variation in water consumption.

However, as more variables are used, R² generally increases.

In such cases, the adjusted coefficient of determination is calculated, which accounts for the sample size and the number of predictor variables.