![]() ![]() There are a few third-party add-ins that provide Analysis ToolPak functionality for Excel 2011. I can't find the Analysis ToolPak in Excel for Mac 2011 Now the Data Analysis command is available on the Data tab. If you get a prompt that the Analysis ToolPak is not currently installed on your computer, click Yes to install it. If Analysis ToolPak is not listed in the Add-Ins available box, click Browse to locate it. In the Add-Ins available box, select the Analysis ToolPak check box, and then click OK. Load the Analysis ToolPak in Excel for MacĬlick the Tools menu, and then click Excel Add-ins. The ToolPak displays in English when your language is not supported. See Supported languages for more information. Some languages aren't supported by the Analysis ToolPak. See I can't find the Analysis ToolPak in Excel for Mac 2011 for more information. In the end this gives significant differences when the tests are performed on the 6 columns.The Analysis ToolPak is not available for Excel for Mac 2011. Furthermore there are non neglectable covariances between RV1 and RV4, between RV2 and RV5 and between RV3 and RV6. Small differences observed on the first 3 columns, and the larger ones observed on the last 3 columns accumulate. However, the tests on the covariance matrices are surprisingly different. The difference between G1 and G2 based on the Mahalanobis distance is slightly lower. Tests on the means yield results very close to the case 2 (see above). In the "Outputs" tab we request correlation and covariance matrices. ![]() This is due to the size of the groups that are too small to distinguish groups for which variances are 5’² and 7’². But the test of Kullback fails to identify the difference. ![]() Regarding the covariance matrices, the Box tests are on the borderline of finding a difference, the p-value being equal to 0.06. It is not surprising that the small difference between the first 2 groups has not been detected as significant, as the group size is too small to identify such a small difference. We notice that the Mahalanobis distances are only meaningful when the group 3 is concerned. In this case, tests on averages identify the difference: the test of the Wilks' Lambda concludes that there is a significant difference between the groups means. This time we select only the last three columns. We note with the Fisher's distances that the distance between G1, on the one hand, and G2 or G3 on the other hand, is greater than the distance between G2 and G3, but still not significant however. The results indicate that for both averages (Wilks test) and covariance matrices (Box and Kullback tests), the three groups can be regarded as identical and from the same population. Select the data corresponding to the first three columns on the Excel sheet, then select column B that contains the group identifiers.Ĭlick OK to launch the computations. When you click on the button, a dialog box appears. Once XLSTAT is activated, select the XLSTAT / Parametric tests / Multidimensional tests command, or click on the corresponding button of the Parametric tests toolbar (see below). Tests on the first three columns Setting up a multidimensional test In order to demonstrate how to use the tool and the relevance of the tests, we will first do a multidimensional test on the first 3 columns, and then on the following 3, and then on the 6 columns together. Testing the difference between samples using the Mahalanobis distance The following three columns have been sampled in a Normal N(2, 5) distribution for G1, in a Normal N(2.2, 5.2) distribution for G2 and in a Normal N(8, 7) distribution for G3. The first three columns are drawn in a standard normal distribution N(0,1). This tutorial is based on artificial data that have been generated with the distribution sampling tool of XLSTAT. Dataset to test the difference between samples using the Mahalanobis distance This tutorial will help you compare samples described by several variables using the Mahalanobis distance in Excel with the XLSTAT software.
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