1. ASQ 1401 Section El Paso, TEXAS 2009 January 14 Rudy Kittlitz

2. ISO/TC 69 Application of Statistical Methods This Technical Committee [TC] has several subcommittees [SC] and working groups [WG] Terminology and symbols Statistical interpretation of data Applications of statistical methods in process management Acceptance sampling Measurement methods and results Six Sigma applications

3. 30 th Plenary Meeting Beijing 2008 October 11-17 Approximately 75 delegates From India, Germany, France, Denmark, United Kingdom, etc.

4. US Technical Advisory Group [TAG] One-day meetings in the spring and the fall Rudy became involved in ISO/TC 69 with the 1995 March meeting of the US TAG His first international meeting was in 1996 June in Stockholm and has attended each year In 2001 Rudy was elected as Chair of the US TAG and has continued ANSI [American National Standards Institute] is the official contact with ISO All USA Delegates represent the USA and ANSI, not their company, university, etc.

5. Meetings in Beijing Arrived Friday night, 10/10 First meeting on Saturday A special committee meeting on Sunday Monday through Friday meetings Took a tour on Tuesday and on Thursday Left on Saturday, 10/18

14. Using Statistics To Answer The Question: “Has The Rate of Category 3+ Atlantic Hurricanes Changed Since 1940?” This is not a talk on Global Warming But a statistical assessment on whether or not the rate (i.e., number of hurricanes per year) of category 3+ Atlantic hurricanes has changed since 1940 Cat 3+ are also known as “Major Hurricanes” Over the past few years all sorts of statements about “cycles of Atlantic hurricanes”, “increased intensity”, and others A simple statistical assessment should be able to answer

16. Initial Comments About The Data Some possible “up and down” for these 68 data points, but has the rate changed? Proper application of statistical analysis and Statistical Process Control (SPC) should be able to answer this question Mean of the data is 2.588 Standard deviation is 1.863

18. The Poisson Distribution The Poisson is a candidate to describe this data It is a count distribution Only need to know the mean or the average For small averages, the positive skew is evident This is seen in Figure 2 The theoretical standard deviation is Observed std dev = 1.893 vs theoretical std dev = 1.609 F-test indicates no significant difference [p = 0.234] A SPC chart of Poisson data is the c-chart

19. Usual Calculations For The c-Chart Upper Control Limit [UCL] UCL = Avg + 3 UCL = 2.588 + 3 = 7.414 Lower Control Limit [LCL] LCL = Avg – 3 LCL = 2.588 – 3 = - 2.238 No LCL since calculated LCL is below zero These simple calculations ignore the skewness of the Poisson

20. Improved Limits For Poisson Data Article in Quality Engineering Kittlitz, R. G. Jr. (2006). Calculating the (Almost) Exact Control Limits for a C-Chart. Quality Engineering, 18:359-366. The improved limits are based on a simple transformation of the original data It is Kittlitz, R. G. Jr. (2003). Transforming The Poisson Distribution To Symmetry For SPC Applications and Other Statistical Analysis. MS Capstone Project for University of Alabama at Huntsville For the hurricane data this transformation produces a skewness of 0.47 vs original of 0.98

21. Improved Limits For Poisson Data Cont’d UCL = LCL = Don’t let these equations scare you! Programmable calculator performs the calculations Improved UCL = 8.07 Improved LCL = No Lower Limit Calculations produces a negative number inside bracket

23. Initial Conclusions From Analysis The typical “run-rules” for an SPC chart did not trigger any signals The 1950 data point of 8 “Cat 3+” hurricanes is close to the limit, but the cumulative probability of 8 for an average of 2.588 is 0.99856 which is less than the limit of 0.99865 Unless the rate changes, we can expect 0 to 8 “Cat 3+” hurricanes per year

24. Some Additional Analysis Advances have been made to detect a shift in the mean of data more efficiently/quicker and is an improvement over the typical “counting rules” Exponentially Weighted Moving Average [EWMA] Cumulative Summation or CUSUM An EWMA chart will be used to analyze the transformed data The 2003 reference details how the transformed mean and the transformed standard deviation can be calculated from the original mean Transformed mean = 1.92443 Transformed standard deviation = 0.777823 The EWMA chart is shown in Figure 4

26. Comments About Figure 4 Remember for an EWMA chart that “counting rules” cannot be used since the points are not independent Likewise the “wandering” of the points are typical of an EWMA chart and no conclusions can be drawn from any apparent “cycles” The only valid signal is if a point exceeds the limits No points exceed either the upper or lower limits Thus, no significant change in the mean

27. Questions?