![]() You should now be able to calculate statistics for skewness and kurtosis in SPSS. You can also see that SPSS has calculated the mean (46.93 metres) and the standard deviation (21.122 metres). The result will pop up in the SPSS output viewer. Once you’ve made your selections, click on Continue, and then on OK in the Descriptives dialog to tell SPSS to do the calculation. You’ll notice that we’ve also instructed SPSS to calculate the mean and standard deviation. To calculate skewness and kurtosis, just select the options (as above). This will bring up the Descriptives: Options dialog box, within which it is possible to choose a number of descriptive measures. Once you’ve got your variable into the right hand column, click on the Options button. You can drag and drop, or use the arrow button, as shown below. You need to get the variable for which you wish to calculate skewness and kurtosis into the box on the right. This will bring up the Descriptives dialog box. To begin the calculation, click on Analyze -> Descriptive Statistics -> Descriptives. We’re going to use the Descriptives menu option. There are a number of different ways to calculate skewness and kurtosis in SPSS. b) The sampling distribution of the mean. calculate the mean for every sample, and construct a graph of the shape of the distribution based on all of the means, what would we have. ![]() The usual reason to do this is to get an idea of whether the data is normally distributed. For what is the variable view in IBM SPSSs data editor used a) Entering data. We’re going to calculate the skewness and kurtosis of the data that represents the Frisbee Throwing Distance in Metres variable (see above). Kurtosis measures the tail-heaviness of the distribution. Skewness is a measure of the symmetry, or lack thereof, of a distribution.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |