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Another great webinar where Jay Arthur shows some of the capabilities of QI Macros to a group of folks who signed up and attended. Some were familiar with the software and already use it, and some had not yet begun to use it; all were interested in learning new ways that QI Macros can help them with their Quality Improvement efforts. (You can hear him answering questions typed in by webinar attendees.)
If you saw a feature demonstrated in the webinar that might have been added to QI Macros after the version you’re using (for instance, the automated Value Stream Map), you may need to purchase an upgrade to bring your QI Macros to the current version.
Continue Reading "8/14/18 QI Macros Webinar"
Posted by Jay Arthur in Data Mining, Excel, QI Macros, Six Sigma, Statistics.
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"
Posted by Jay Arthur in Improvement Insights, QI Macros, Six Sigma, Statistics.
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"
Posted by Jay Arthur in Improvement Insights, QI Macros, Statistics.
I’m here at the IISE (Institute of Industrial and Systems Engineers) conference in Pittsburgh.
One professor had been teaching students how to use Excel to create control charts, but he was beginning to feel like that was a waste of classroom time (duh!).
I beat him up a little for teaching DIY Excel stuff to students. If the professor does it, they think that’s how it’s done. With QI Macros he can get them right into analysis.
I feel the same way at ASA (American Statistical Association) when they use “R” to do statistics. Sure it’s free, but should statisticians be programming in “R” or just using software to achieve the same result.
Continue Reading "Are We Teaching Students the Unnecessary Things?"
Posted by Jay Arthur in healthcare, Lean, Manufacturing, Service, Six Sigma, Statistics.
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"
Posted by Jay Arthur in QI Macros, Six Sigma, Statistics.
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?"
Posted by Jay Arthur in QI Macros, Six Sigma, Statistics.
At IHI, I spoke with one Quality Improvement Adviser who was getting blow back from certain statisticians in her organization. They were challenging her methods and tools and analysis.
As I have written before, I call these folks “Stat Bastards” because they belittle others rather than help them.
As a member of the American Statistical Association, almost every statistician I meet is incredibly kind, generous and helpful. The few that flaunt their training rather than helping are impeding the forward progress of their organization.
I say, if they have better data, bring it. If they have better analysis, bring it. If they have better tools, bring it.
Continue Reading "Statistical Bullies"
Posted by Jay Arthur in Six Sigma, Statistics.
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"
Posted by Jay Arthur in QI Macros, Statistics.
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"
Posted by Jay Arthur in QI Macros, Six Sigma, Statistics.
Creating homemade Cp and Cpk templates often results in incorrect values. There are many, many mistakes you might make without realizing. Here are a few examples.
A customer sent me their home grown template for calculating Cp and Cpk and wondered why the QI Macros got such radically different values. It was easy to see from their data that they were using standard deviation, not Sigma estimator (Rbar/d2) to calculate Cp and Cpk. Use Stdev to calculate Pp and Ppk, not Cp and Cpk:
They had run the QI Macros histogram on two columns of data, one measured at 0 degrees and one measured at 90 degrees.
Continue Reading "Cp Cpk Formulas and the Mistakes in Homemade Templates"
Posted by Jay Arthur in Manufacturing, QI Macros, Six Sigma, Statistics.
