Clicking OK generates the information shown in Figure \(\PageIndex \beta_0 \neq 0\) Select the radio button for Output range and click on any empty cell this is where Excel will place the results. Excel’s summary output uses the x-axis label to identify the slope. The final equation should be:Īnnual sales = 1167.8 + 19993.Including labels is a good idea. You can see that all of the values are less than 0.05 now. Read More: How to Create One-Variable Data Table in Excel 2013 – Remove Motivation from independent variablesĪfter deleting Motivation as the independent variable, I applied the same approach and did a simple regression analysis. For our problem, it is better for us to discard motivation when considering independent variables. But you also need to check p-values in range I17: I19 to see if constant and independent variables are useful for the prediction of the dependent variable. Only if p-value in cell J12 is less than 0.05, the whole regression equation is reliable. However, to see if the results are reliable, you also need to check p-values highlighted in yellow. The equation should be Annual sales = 1589.2 + 19928.3*(Highest Year of School Completed) + 11.9*(Motivation as Measured by Higgins Motivation Scale). And coefficients (range F17: F19) in the third table returned you the values of constants and coefficients. The higher R-square (cell F5), the tight relationship exists between dependent variables and independent variables. It is better to always put the dependent variable (Annual sales here) before the independent variables. Read More: How to Calculate/Find Mean and Standard Deviation in Excel Set Up ModelĪnnual sales, highest year of school completed and Motivation was entered into column A, column B, and column C as shown in Figure 1. Therefore, the equation will be:Īnnual sales = constant + β1*(Highest Year of School Completed) + β2*(Motivation as Measured by Higgins Motivation Scale) After you get values of constant, β1, β2… βn, you can use them to make the predictions.Īs for our problem, there are only two factors in which we have an interest. The change in Y each 1 increment change in xnĬonstant and β1, β2… βn can be calculated based on available sample data. The change in Y each 1 increment change in x2
The change in Y each 1 increment change in x1 Here are the explanations for constants and coefficients: Y And this kind of linear relationship can be described using the following formula: Generally, multiple regression analysis assumes that there is a linear relationship between the dependent variable (y) and independent variables (x1, x2, x3 … xn). Motivation as Measured by Higgins Motivation Scale Whether education or motivation has an impact on annual sales or not? Highest Year of School Completed Suppose that we took 5 randomly selected salespeople and collected the information as shown in the below table.
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