The Forgotten Tabs: Means Analysis


Continuing with the Forgotten Tabs series, the next tab we’ll be focusing on is the Means Analysis tab. The Means Analysis tab provides the mean, number of observations, maximum values, and minimum values for any of the variables in your dataset. You also have the option to take “means by” and “subclass by” to view the means of multiple subcategories across variable combinations.


In this case, we’re comparing the attrition rates of legacy and non-legacy students by whether or not they received financial aid. For each of these four possible categories, we are able to see the mean attrition rate, the number of observations, and the min and max values. Doing so allows us to see the differences in attrition rate over a couple of different characteristics.

The Means Analysis tab can be also useful in comparing data from different cohorts or years in order to spot trends. In the example below, we’re comparing attrition rates by year, which allows us to pick up on any trends or changes that are occurring from year to year. If for some reason we were noticing a year that had a much higher or lower attrition rate than the other years, this gives us the opportunity to pick up on that and investigate further as to why that might be.



You might also note that beyond looking at ‘Attrition’, we are also looking at a variable called ‘Predicted Attrition’. This variable represents the predicted attrition probabilities that we’ve assigned to each student. In this case, we’ve grouped these values by year to get an idea of how well we’re predicting attrition for that year compared to the actual attrition rate. Comparing our predicted values to actual values gives us a sense of any weaknesses from year to year that our predictive model might have. If we do find any weakness in predictive ability, we have the opportunity to go back and further fine-tune our model in order to incorporate our findings.


-Caitlin Garrett, Statistical Analyst at Rapid Insight

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Decentralize analytics. Harness the power of many.