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Move beyond spreadsheets to data mining, forecasting, optimization – and more

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Mike Gilliland 0
In Defense of Outliers

If outliers could scream, would we be so cavalier about removing them from our history, and excluding them from our statistical forecasting models? Well, maybe we would – if they screamed all the time, and for no good reason. (This sentiment is adapted from my favorite of the many Deep

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Rick Wicklin 0
The module that vanished

Recently, I needed to detect whether a matrix consists entirely of missing values. I wrote the following module: proc iml; /** Module to detect whether all elements of a matrix are missing values. Works for both numeric and character matrices. Version 1 (not optimal) **/ start isMissing(x); if type(x)='C' then

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Rick Wicklin 0
Sampling with replacement

Sampling with replacement is a useful technique for simulations and for resampling from data. Over at the SAS/IML Discussion Forum, there was a recent question about how to use SAS/IML software to sample with replacement from a set of events. I have previously blogged about efficient sampling, but this topic

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Mike Gilliland 0
Mistakes in the Forecasting Hierarchy

Many forecasting software packages support hierarchical forecasting. You define the hierarchical relationship of your products and locations, create forecasts at one or more levels, and then reconcile the forecasts across the full hierarchy. In a top-down approach, you generate forecasts at the highest level and apportion it down to lower

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Rick Wicklin 0
Tricks and Treats

How can you change a programming trick into a programming treat? Try this algorithm: If you develop a clever snippet of code, squirrel it away. This snippet is a "trick." If you use the trick a second time, copy and modify the code. The trick has become a "treat." If

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