#CALCULATE RESIDUAL HOW TO#
Here’s an example of how to find your residual income: In fact, the formula for residual income is simple: Just subtract your monthly expenses, like your mortgage, car payments and student loans, from your monthly income. You don’t need to be a math whiz to calculate residual income. But residual income may not always come from passive income sources. You could use passive income streams to increase your monthly residual income. And it can include rental property profits, dividend stocks or other sources. Passive income is income that you earn from little or no effort. You might have seen the term “passive income” used interchangeably with “residual income.” But this article will use these two terms differently. This extra money can go toward things like investments, debt payoffs, savings or even a vacation fund. Residual income refers to the money you have after you’ve taken care of ongoing expenses like your mortgage, credit card bills, utilities, groceries and car payments. For those who are more interested in learning to use audio-visuals, “Kanda Data” has prepared a video tutorial.Think of residual income like you’d think of Thanksgiving leftovers. 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.
#CALCULATE RESIDUAL SERIES#
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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