Improvement Insights Blog
Latest "Statistics" Posts
Everyone thinks I’m a statistician by trade, but nothing could be further from the truth. Here’s my journey.
“Hi, this is Jay Arthur, author of “Lean Six Sigma Demystified” and the QI Macros [software]. I’m here in Maui Kaanapali Villas in Maui.
“One of the things that I want you to think about is because I wrote the QI Macros, everybody thinks I’m a statistician or something, but… the truth is a little bit further from that.
“Suzuki the Zen master had a story of four horses: One ran easily, one you had to flog it a little bit, one he had to flog a little more, and the fourth one you had to flog a lot to get it to move.
Continue Reading "Jay is the Fourth Horse of Statistics"
At the 2018 Magnet Nursing conference in Denver, I saw many improvement posters using line or bar charts with an added trend line to show improvement. Unfortunately, few of the trends were valid. Here’s why:
Continue Reading "Trendlines Are Rarely Improvements"
Too many quality professionals are clinging to the way things have always been done. There’s some sort of taboo about doing things quickly and easily. What are the differences between 20th and 21st Century Quality? Watch and find out:
Continue Reading "20th vs 21st Century Quality"
Statisticians say “correlation does not equal causation,” but what if we can prove cause and effect using new tools? The Book of Why, by Judea Pearl explains how this new science of cause and effect has shifted statistics over the last 30 years.
Continue Reading "The Book of Why?"
I’ve noticed a disturbing trend. Customers call us wanting to know all of the background about the hows, whys and formulas of a chart. I think they are afraid someone will challenge or ridicule their analysis. Here’s what I think.
Continue Reading "Fear of Ridicule and What to Do About It"
People have been trying to make statistics simple and easy to understand for decades.
But statistics aren’t simple. Maybe we should change how we teach them?
Continue Reading "Statistics are Simple"
I have found that an XmR chart is the easiest way to display attribute data. Simply convert the numerator/denominator into a ratio and plot the ratio.
- defects per day could be a c chart, but an XmR chart works just as well
- defects/samplesize could be np, p or u chart, but XmR chart works just as well using the ratio
Almost two decades ago, Tom Pyzdek said: X chart provides an excellent approximation to the p chart.
More recently, Donald Wheeler noted that XmR chart limits will be very close to c, np, p or u chart limits if the underlying distribution is correct.
Continue Reading "Use XmR Charts instead of c, np, p and u Charts"
Customer asked me what seemed like a strange question: What specification limits do I need to get a Cp greater than one? Usually her customer should set specification limits, but her boss wanted to know what they could deliver. Hmmmm!
Then I realized that since QI Macros templates (e.g., XmR chart) calculate the average and sigma estimator, the LSL/USL for Cp = 1.0 would be:
LSL = Average – 3*SigEst USL = Average+ 3*SigEst
For Cp = 1.33, just change the 3 to a 4; Cp = 1.66, change the 3 to a 5. Here’s an XmR chart template with some sample data and calculations to reverse engineer spec limits:
Continue Reading "What USL/LSL Do I Need for Cp>1?"
Renu Davi, Sr. Program Manager for Excel, reported at Excelapaloosa that two-thirds of the 650 million users use Excel to make lists. The other third of users do deeper analysis, but I’m wondering how? Microsoft keeps tabs on Excel usage. Here’s some statistics for usage in an Excel workbook:
- Only 4.3% have a function like SUM(A1:B2)
- 7.5% have Pivottables
- 17% use Freeze Panes
- 54% use Merged Cells (instead of Center Across Selection which works much better in many ways)
I wonder why we need any new features if the vast majority of users don’t or won’t use them. Maybe we need to invest in teaching people how to use the Excel features they have.
Continue Reading "Users Barely Using Excel"
The June, 2016 HRB article by Scott Berinato examines how to use charts and diagrams to express ideas and statistics. I agree with Anmol Garg, Tesla data scientist quoted in the article, “You can’t find anything looking at spreadsheets and querying databases. It has to be visual.”
Bernato says: “Convenient is a tempting replacement for good, but it will lead to charts that are merely adequate or, worse, ineffective.” He separates visualizations into four components: idea generation, idea illustration, visual discovery and everyday dataviz. Simple line, bar and pie charts are great for idea generation and illustration, but terrible for visual discovery and dataviz.
Continue Reading "Data Visualizations that Really Work"