Posts Tagged ‘interaction’

Where Do Those Six Sigma Statistics Come From?

Friday, January 13th, 2012

A student of mine had numerous questions about the various statistics used in Six Sigma. Here is my response to him in an open email:

The questions you are asking regarding “Where do these statistics come from?” require entire courses in statistics to answer. In Lean Six Sigma we take information from a dozen or so statistics courses, project management courses, psychology courses, business courses, mathematics courses, etc. and put it into an action framework that can be used to make fast improvements. We probably present less than 10% of the information you would receive if you sat through all of these courses, but we do so in less than 5% of the time it would take to complete all of these courses. It’s a tradeoff. We make the greatest compromises in the field of statistics. We discuss the use and interpretation of a select subset of statistics, and answer the question “where do these statistics come from?” by saying “they come from computer software.” While most are satisfied with this answer, some find the answer to be most unsatisfying. Judging from your questions, I suspect you are in the latter group.

anova-table-calculations-e-handbook-of-statistics

Two-Way ANOVA Calculations from E-Handbook of Statistics

Assuming you don’t have the time or the desire to take all of the courses relating to the Lean Six Sigma body of knowledge, but still seek answers to the specific statistics you asked about, I recommend the E-Handbook of Statistical Methods. This reference source is free and very comprehensive. It’s easy to search and will give you the answers you seek. For example, I searched on the term sum of squares, which you asked about, and the search returned pages on the half-normal probability plot (your question about fig. 10.26,) 1-way ANOVA (several of your question were about these calculations,) and several other related topics. A search on ss interaction provides answers to your question about the calculation of this intermediate statistic.

Sorry I can’t address all of your questions via email, but perhaps the reference above will start you on your way to answers. I had the same questions when I started learning about quality improvement nearly 45 years ago, and I am still looking for answers to questions today. Have fun!

Tom Pyzdek

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Design of Experiments and Baseball

Monday, August 31st, 2009

A Black Belt steps up to the plate with Six Sigma confidence.

Bill had a problem. His company’s baseball team wasn’t doing that well, and he was part of the reason. Bill was in a long slump. Frankly, he stunk at the plate.

But Bill is a Six Sigma Black Belt. He decided to approach his batting problem just like he would approach any process problem at work–by conducting a designed experiment. First, Bill determined which factors are important. He wrote up a lengthy list and then winnowed it down to four experimental variables (see Table 1).

Table 1: Experimental Variables for Hitting

Bill decided to spend a few evenings and weekends on the practice field swinging at 100 pitches for each of the 16 combinations of the four variables needed to conduct a full-factorial experiment. The field was equipped with a pitching machine that could be programmed to throw pitches at either 60 mph or 80 mph. Bill decided to count any ball that went past the infield in fair territory as a hit. Over a two-week period Bill was able to complete the experiment, producing the results shown in Table 2.

Table 2: Bill’s Batting Experiment

The analysis indicates that factors B and D, and especially the C-D interaction, make big differences in Bill’s performance. Factors A and C do not have a significant effect on Bill’s batting average. The analysis in Table 3 shows the details.

Table 3: Significant Factor Effects

The 95-percent confidence interval for C (position in the batter’s box) includes zero, meaning that C is not statistically significant as a main effect. (C is included because the significant C-D interaction term requires it for statistical reasons.) However, the other factors in the table–B (choke on the bat) and D (speed of the pitch)–are statistically significant. The most important factor is the C-D interaction, which has an impressive effect of more than 9 percent. The coefficient estimate tells us what happens to Bill’s batting average as we go from one level of the variable to another. For example, when B is at the high level (choke up on the bat two inches), Bill’s batting average improves by about four percentage points.

The analysis indicates that when Bill is facing a pitcher with real heat (80 mph isn’t too bad for an amateur pitcher), he can improve his batting average from 8 percent to 28.75 percent by standing near the back of the batter’s box (see Table 4). Conversely, when Bill is up against a 60-mph hurler, he’s better off in the front of the batter’s box (38.75 percent in front hits vs. 15 percent in back). Combining all of these results, Bill’s strategy is to always choke up on the bat and position himself in the batter’s box depending on the expected speed of the pitch.

Table 4: Bill’s Results

Bill may not be ready for the majors with this strategy, but he’s hitting a lot better than the .206 (20.6%) he’d been getting without a strategy. In the meantime, Bill, work on hitting that fast ball!

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