Using Design of Experiments to Bake a
Better Cookie
That batch of cookies that just
came out of your oven looks nothing like the pictures in
the cookbook. It’s vexing enough when an ordinary batch
fails to meet your expectations, but when you’re making
cookies for the holidays the disappointment can approach
traumatic intensity. Who wants to eat a cutout cookie
that looks more like a melting snowball than a snowman?
When the first batch of cookies
Bill Howell made last year for family and friends
produced…well…mixed results, he applied the power of
Minitab to find a solution.
P charts and
U charts have been a valuable tool in the quality
engineer's toolbox for decades. But, as David Laney
found out, when sample sizes are very large, the control
limits become too narrow and the data can spill out over
the control limits.
Where some saw chaos, Laney was
inspired to apply the teachings of Fisher, Deming,
Wheeler, and others to the problem and ended up changing
how we think about P charts and U charts.
As we were incorporating Laney's
P’
Charts and U’ Charts into the latest update of
Minitab 16 Statistical Software, we wanted to learn more
about how they came about.
It's not every day you get to
learn the story behind a new statistical method from the
person who created it, but Minitab recently had a chance
to talk with Laney about his inspirations and about the
P’ Charts and U’ Charts that bear his name.
Analysis
with Minitab Reveals Previously Unknown Phase of Bowling
Ball Motion
When researchers at the U.S.
Bowling Congress (USBC) set out to assess a
specification for static weight in bowling balls, they
didn’t expect to redefine the way balls travel down the
bowling lane.
But a designed experiment and
statistical analysis done with Minitab Statistical
Software revealed a previously unknown phase of bowling
ball motion, giving the researchers a new set of
questions to pursue—and giving the USBC solid evidence
that keeping the specification would help keep bowling
fair.
The
Ascent to Everest: Exploratory
Statistics Last month, thousands
of trekkers were stranded by bad weather near
Mount Everest and had to be evacuated.
The news made me wonder: Just how many people
are chillin' on Chololungma ( "Holy Mother"—the
Tibetan name for Everest) these days? So I decided
to do some exploring of the statistical variety,
using Minitab as my trusty Sherpa.
P charts and
U charts have been a valuable tool in the quality
engineer's toolbox for decades. But, as David Laney
found out, when sample sizes are very large, the control
limits become too narrow and the data can spill out over
the control limits. 
Analysis
with Minitab Reveals Previously Unknown Phase of Bowling
Ball Motion 