Quality Digest’s picture

By Quality Digest

Misadventures in Big-Box Land

Regarding “Big Boxes Beware” (“First Word,” Dirk Dusharme, November 2008): If you want to experience grocery-industry customer service at its finest, visit Wegmans. Their employees will load your groceries in your car for free, provide umbrellas for trips to your car in the rain for those who don’t have one, and personally take you to the items that you’re looking for when you can’t find something.

Those needing a lesson in customer service should shop there a time or two. They will learn what service is all about.

--Chris Wallen

 

Hear! Hear! My boss was commenting today that he went to “Wally World” on his way to work and tried to buy light bulbs. First of all, he was looking for 100-watt bulbs and the closest that they sell now are the 90-watt kind.

When he went to check out, the register sign was lit up, but no one was there to run the register. He stepped back far enough to see two workers stocking an end cap. They looked his way three times and still went back to stocking.

On the third time, my boss said he even waved at them so that they would see movement at the register. Needless to say, when he left, the light bulbs were still on the counter.

--Mike Janes

 

Donald J. Wheeler’s picture

By Donald J. Wheeler

Many have been taught that they must remove outliers prior to analysis. This is because much of modern statistics is concerned with creating a mathematical model for the data. Because all these models are created using algorithms, they tend to be severely affected by any unusual or extreme values.

Therefore, to use these mathematical techniques to obtain useful and appropriate models, it’s often necessary to polish up the data by removing the outliers. However, the act of building a model implicitly assumes that the data are homogeneous enough to justify the use of a model.

For example, the histogram in figure 1 has a bell-shaped curve superimposed. This curve is based on the average and standard deviation statistic for all 100 values in the histogram. It’s neither wide enough nor tall enough to provide a good fit to the data. The histogram in figure 2 contains the 93 values left after the seven extreme values (the four lowest and three highest) were deleted. Now the curve based on the average and the standard deviation statistic does a much better job of fitting the data. Thus, it’s true that outliers can undermine our efforts to create a model for our data.