The metric is used by companies to help determine whether to pursue certain projects or not. In corporate finance, the term “residual income” is defined as the operating income generated by a project or investment in excess of the minimum required rate of return. How to Calculate Residual Income (Step-by-Step) ![]() For those who are more interested in learning to use audio-visuals, “Kanda Data” has prepared a video tutorial.Residual Income measures the excess net operating income earned over the required rate of return on a company’s operating assets. This assumption must be met so that the regression estimation results produce the Best Linear Unbiased Estimator (BLUE). One of the simple linear regression assumptions that must be met is that the residuals are normally distributed. After calculating all residual values, we can test for normality. For example, based on two decades of annual sales data, we can forecast sales data for the next few years using this simple linear regression.īased on the calculation of the first observation, we get a residual value of 2.997. Based on the calculations, we can determine the difference between the actual and predicted values of the regression estimation results for the dependent variable. This regression estimation can use historical data/time series data and cross-section data. Next, you need to calculate residual values for all observations/samples in your study. You have successfully calculated the residual value for the first observation/sample from these calculations. The formula to calculate it can be seen in the following equation:įor example, if the Actual Y value is 213, then you can calculate the residual value as follows: The residual value is the difference between the actual observed value of the dependent variable (Y) and the predicted Y value. Before calculating the residual value, you should know the definition of residual value in regression analysis. Referring to the beginning of the paragraph, Y predicted is used to calculate the residual value in a simple linear regression analysis. Congratulations, you have successfully calculated the Y predicted value correctly. In the same way, you can calculate the predicted Y value for all existing observation data or sample data. You can use Microsoft Excel to simplify and save time in calculating Y predicted. So, how to calculate the predicted Y for this 1st observation is: Furthermore, the first observation value for the variable X is 6000. The way to calculate it is by adding and multiplying each coefficient of the estimation result with the initial observation value of the independent variable.įor example, if the intercept value is 218.38 and the estimated coefficient value for the X variable is -0.0014. The predicted Y value can be calculated for each observation based on this equation. ![]() These two values will be used to calculate the Y Predicted value.Īs we already know, the general equation for simple linear regression is: In last week’s article, a tutorial was given on calculating the coefficients of the regression parameters, namely the intercept (bo) value and the b1 coefficient. This model only consists of one dependent variable and one independent variable. Therefore, we will discuss how to calculate the predicted Y value and residual value on this occasion.įirst, we will find out how to get the predicted Y value in simple linear regression. ![]() However, before calculating the residual value, you must first calculate the predicted Y value. In addition, the linear regression of the ordinary least square method must pass the assumption test that the residuals must be normally distributed. The residual value in linear regression analysis needs to be calculated first before calculating the variance.
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