# Summaries or graphs?

Herb Edelstein from Two Crows consulting introduced me to this neat example showing how graphs are much more revealing than summary statistics. This is an age-old example by Anscombe (1973). I will show a slightly updated version of Anscombe’s example, by Basset et al. (1986):We have four datasets, each containing 11 pairs of X and Y measurements. All four datasets have the same X variable, and only differ on the Y values. Here are the summary statistics for each of the four Y variables (A, B, C, D): A B C D Average 20.95 20.95 20.95 20.95 Std 1.495794 1.495794 … Continue reading Summaries or graphs?

# Resources for instructors of data mining courses in b-schools

With the increasing popularity of data mining courses being offered in business schools (at the MBA and undergraduate levels), a growing number of faculty are becoming involved. Instructors come from diverse backgrounds: statistics, information systems, machine learning, management science, marketing, and more. Since our textbook Data Mining for Business Intelligence has been out, I’ve received requests from many instructors to share materials, information, and other resources. At last, I have launched BZST Teaching, a forum for instructors teaching data mining in b-schools. The forum is open only to instructors and a host of materials and communication options are available. It … Continue reading Resources for instructors of data mining courses in b-schools

# Student network launched!

I recently launched a forum for the growing population of MBA students who are veterans of our data mining course. The goal of the forum is to foster networking, job-related communications (not too many data mining-savvy MBAs out there!), to share interesting data analytic stories, and to keep in touch (where are you today?). Continue reading Student network launched!

# Weighted nearest-neighbors

K-nearest neighbors (k-NN) is a simple yet often powerful classification / prediction method. The basic idea, for predicting a new observation, is to find the k most similar observations in terms of the predictor (X) values, and then let those k neighbors vote to determine the predicted class membership (or take their Y average to predict their numerical outcome). Since this is such an intuitive method, I thought it would be useful to discuss two improvements that have been suggested by data miners. Both use weighting, but in different ways. One intuitive improvement is to weight the neighbors by their … Continue reading Weighted nearest-neighbors