![]() Based on this output, you will find the predicted Y and residuals at the bottom, as shown below: How to Calculate Durbin Watson Statistics in Excelīased on the Durbin Watson test calculation template using excel, the predicted Y and residual values can be directly inputted into the calculation template. In detail, the steps to bring up the predicted Y and residuals can be seen in the image below:Īfter you click OK, then the analysis output will appear. If you have activated “Residuals”, the predicted Y and residuals will appear in the analysis results. One important thing that needs to be done is to enable “Residuals”. You can save analysis results in the same sheet, new worksheet ply, or new workbook. In the same way, input all data of variable X, including its label, to “Input X Range”.Įnable labels and set confidence levels to 95%. Then input all variable Y data, including its label, to “Input Y Range”. After clicking regression, a new window will appear. In the next stage, you click regression from several analysis tools provided by Excel. If you cannot find it, please activate the data analysis toolpak following my tutorial article entitled: “ How to Activate and Load the Data Analysis Toolpak in Excel“. You can click “Data”, then in the upper right corner is an option “Data Analysis”. You can use the analysis data toolpak in Excel. However, there is an easy and fast way that researchers can use to find the predicted Y and residual values in excel. To manually calculate the estimated coefficients bo, b1, and b2, you can read my previous article entitled: “ Finding Coefficients bo, b1, b2, and R Squared Manually in Multiple Linear Regression“.įurthermore, you only need to subtract the actual Y value with the predicted Y to calculate the residual value. Predicted Y can only be calculated if the estimated coefficients bo, b1, and b2 have been obtained. Researchers can easily calculate the predicted Y and residual values in excel. In detail, the template for calculating the Durbin-Watson statistics can be seen in the table below: How to find Predicted Y and Residual values in Excel It will be easier to make a calculation template using Excel to calculate the Durbin-Watson statistics. Durbin Watson statistics were obtained by dividing the squared residual value (e t^2) by (e t – e t-1)^2. The formula used to calculate the statistical Durbin-Watson value includes several components that need to be calculated first, namely predicted Y, residual (e t), the difference between the residuals in period t and the previous period (e t – e t-1), residual squared (e t^2), and (e t – e t-1)^2. In detail, the data that researchers have collected can be seen in the table below: The Formula for Calculating Durbin Watson Statistics X 2 = Marketing Staff – Person (independent variable)ī 1, b 2 = Regression estimation coefficientsīased on the specifications of the equations that the researcher has prepared, the researcher inputs the data collected in Excel. X 1 = Advertising Cost – USD (independent variable) Y = Product sales – Units (dependent variable) Researchers took quarterly time series data from as many as 15 observations.īased on the data that has been collected, the researcher then specifies the equation as follows. ![]() Based on the case examples, the researcher aims to determine the effect of advertising costs and marketing staff on product sales. I have prepared mini research materials as a practice for the Durbin Watson test. On this occasion, Kanda Data will discuss the Durbin-Watson autocorrelation test. Several ways can detect autocorrelation, including Durbin Watson test, Lagrange multiplier test, Breusch Godfrey test, and rank test. In comparison, cross-section data does not need to be tested for autocorrelation. One thing that needs to be considered by researchers is that the autocorrelation test is conducted on time series data. The autocorrelation test must be conducted to obtain the best linear unbiased estimator. If there is a correlation, then it is called an autocorrelation problem. The autocorrelation test aims to test whether there is a correlation between residuals in the t period and the previous period (t-1) in the linear regression model. One of the assumption tests required in the regression is the autocorrelation test.Īutocorrelation tests can be done in both simple and multiple linear regression. Researchers who use time series data in linear regression analysis with the OLS method need to conduct some of the required assumption tests. ![]()
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