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Course Spotlight: Spatial Statistics Using R

Have you ever needed to analyze data with a spatial component? Geographic clusters of disease, crimes, animals, plants, events?Or describing the spatial variation of something, and perhaps correlating it with some other predictor? Assessing whether the geographic distribution of something departs from randomness? Location data is ubiquitous, as are maps drawn by GIS software. SkilledContinue reading “Course Spotlight: Spatial Statistics Using R”

“Money and Brains” and “Furs and Station Wagons”

“Money and Brains” and “Furs and Station Wagons” were evocative customer shorthands that the marketing company Claritas came up with over a half century ago. These names, which facilitated the work of marketers and sales people, were shorthand descriptions of segments of customers identified through statistical cluster analysis. Cluster analysis is also used in marketContinue reading ““Money and Brains” and “Furs and Station Wagons””

Course Spotlight: Text Mining

The term text mining is sometimes used in two different meanings in computational statistics: Using predictive modeling to label many documents (e.g. legal docs might be “relevant” or “not relevant”) – this is what we call text mining. Using grammar and syntax to parse the meaning of individual documents – we use the term naturalContinue reading “Course Spotlight: Text Mining”

SAMPLE

Why sample? A while ago, sample would not have been a candidate for Word of the Week, its meaning being pretty obvious to anyone with a passing acquaintance with statistics. I select it today because of some output I saw from a decision tree in Python.

OVERFIT

As applied to statistical models – “overfit” means the model is too accurate, and fitting noise, not signal. For example, the complex polynomial curve in the figure fits the data with no error, but you would not want to rely on it to predict accurately for new data: