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Chan, Tony F.; Golub, Gene H.; Le Veque, Randall J. Algorithms for Computing the Sample Variance: Analysis and Recommendations. Version info: Code for this page was tested in Stata 12.It is not obvious that the method is correct even in exact arithmetic. Comparison of Several Algorithms for Computing Sample Means and Variances. Update: See this page for an extension of this code that supports computing skewness and kurtosis as well.

There is a way to compute variance that is more accurate and is guaranteed to always give positive results.The table below shows the average numbers of awards by program type and seems to suggest that program type is a good candidate for predicting the number of awards, our outcome variable, because the mean value of the outcome appears to vary by prog.tabstat num_awards, by(prog) stats(mean sd n) Summary for variables: num_awards by categories of: prog (type of program) prog | mean sd N --------- ------------------------------ general | .2 .4045199 45 academic | 1 1.278521 105 vocation | .24 .5174506 50 --------- ------------------------------ Total | .63 1.052921 200 ---------------------------------------- Below we use the poisson command to estimate a Poisson regression model. before prog indicates that it is a factor variable (i.e., categorical variable), and that it should be included in the model as a series of indicator variables.For the purpose of illustration, we have simulated a data set for Example 3 above.In this example, num_awards is the outcome variable and indicates the number of awards earned by students at a high school in a year, math is a continuous predictor variable and represents students’ scores on their math final exam, and prog is a categorical predictor variable with three levels indicating the type of program in which the students were enrolled.




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