# Collection of continuous variables with >70% more zeros

This is dataset that is going to be data mined for factors that affect an output that of interest

A large Part of my dataset (150 of 300 potential inputs) has a heavy skew of Zero values. usually this is >90% and always >70% zeros (true zeros)

This has risen as the input variables are usually OR options. -Say i have 10 input variables of the above in a wider dataset. of these 10 any one data line will have approximately 1-4 of these 10 populated with non zeros.

In real world terms if you use input variable 1 you may not need variable 2 to 10 the above statement isnt absolute. you may use a some of input 1 and some of 2 and none of 3 to 10.

i was tempted to transform these into binary catagorical 0 = No 1 = Yes but i would loose the scale on the non zeros. -If input variable 1 IS NOT 0 the values may range between 2 orders of magnitude

– whuber
Jun 30, 2014 at 15:49
• Sorry, i seem to have completely omitted that. Is there standard practice for this type of data? Do i need to transform or otherwise deal with this type of data in a particular way? Ie is categorizing the data a viable method? Or by doing this is the loss of the remaining 10% continuous information important? Jun 30, 2014 at 16:12
• If you have 70% zeros your data are not continuous. If they're continuous apart from the 0s that would be a mixed distribution. Jun 30, 2014 at 23:29
• yes this was the term i needed. It is a mixed distribution Jul 1, 2014 at 7:42