The multiple regression formula with two predictor variables is

Multiple regression formula is used in the analysis of relationship between dependent and multiple independent variables and formula is represented by the equation Y is equal to a plus bX1 plus cX2 plus dX3 plus E where Y is dependent variable, X1, X2, X3 are independent variables, a is intercept, b, c, d are slopes, and E is residual value.
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Relevance and Use
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Source: Multiple Regression Formula (wallstreetmojo.com)

Where,

Y= the dependent variable of the regression
M= slope of the regression
X1=first independent variable of the regression
The x2=second independent variable of the regression
The x3=third independent variable of the regression
B= constant

Multiple Regressions are a method to predict the dependent variable with the help of two or more independent variables. While running this analysis, the main purpose of the researcher is to find out the relationship between the dependent variable and the independent variables. In order to predict the dependent variable, multiple independent variables are chosen, which can help in predicting the dependent variable. It is used when linear regression is not able to do serve the purpose. Regression analysis helps in the process of validating whether the predictor variables are good enough to help in predicting the dependent variable.

Examples

Example #1

Let us try and understand the concept of multiple regressions analysis with the help of an example. Let us try to find out what is the relation between the distance covered by an UBER driver and the age of the driver and the number of years of experience of the driver.

For the calculation of Multiple Regression, go to the Data tab in excel, and then select the data analysis option. For the further procedure and calculation refers to the given article here – Analysis ToolPak in ExcelExcel's data analysis toolpak can be used by users to perform data analysis and other important calculations. It can be manually enabled from the addins section of the files tab by clicking on manage addins, and then checking analysis toolpak.read more

The regression formulaThe regression formula is used to evaluate the relationship between the dependent and independent variables and to determine how the change in the independent variable affects the dependent variable. Y = a + b X +read more for the above example will be

y = MX + MX + b
y= 604.17*-3.18+604.17*-4.06+0
y= -4377

In this particular example, we will see which variable is the dependent variable and which variable is the independent variable. The dependent variable in this regression equation is the distance covered by the UBER driver, and the independent variables are the age of the driver and the number of experiences he has in driving.

Example #2

Let us try and understand the concept of multiple regressions analysis with the help of another example. Let us try to find out what is the relation between the GPA of a class of students and the number of hours of study and the height of the students.

For the calculation, go to the Data tab in excel and then select the data analysis option.

The regression equation for the above example will be

y = MX + MX + b

y= 1.08*.03+1.08*-.002+0

y= .0325

In this particular example, we will see which variable is the dependent variable and which variable is the independent variable. The dependent variable in this regression is the GPA, and the independent variables are study hours and height of the students.

Example #3

Let us try and understand the concept of multiple regressions analysis with the help of another example. Let us try to find out what is the relation between the salary of a group of employees in an organization and the number of years of experience and the age of the employees.

For the calculation, go to the Data tab in excel and then select the data analysis option.

The regression equation for the above example will be

y = MX + MX + b
y= 41308*.-71+41308*-824+0
y= -37019

In this particular example, we will see which variable is the dependent variable and which variable is the independent variable. The dependent variable in this regression equation is the salary, and the independent variables are the experience and age of the employees.

Relevance and Use

Multiple regressions is a very useful statistical method. Regression plays a very role in the world of finance. A lot of forecasting is done using regressionRegression Analysis is a statistical approach for evaluating the relationship between 1 dependent variable & 1 or more independent variables. It is widely used in investing & financing sectors to improve the products & services further. read more analysis. For example, the sales of a particular segment can be predicted in advance with the help of macroeconomic indicators that has a very good correlation with that segment.