popular culture – Synergistic Statistical Consulting, Analysis, Arboronics

Hot Cross Buns: How Much Bang for the Buck?

Good Friday and Easter Monday are public holidays in Australia and UK (the former day is holiday in US in 12 states). For many down here, including those who don’t pay much nevermind to symbols, Good Friday is traditionally the day to eat Hot Cross Buns. For the last few years, the Melbourne Age newspaper has rated a dozen such buns for quality, as well as listing their price.

http://www.goodfood.com.au/good-food/search.html?ss=Good+Food&text=bunfight&type=

We would expect that, quality would increase, to some extent with price, although it would eventually flatten out (e.g. thrice as expensive doesn’t always mean thrice as good). Graphing programs such as Graphpad, Kaleidagraph and SigmaPlot, as well as R and most Stats packages, can readily fit a plethora of polynomial and other nonlinearities, but I used Stata to perform a preliminary scatterplot of the relationship between tasters’ score (out of 10) and price per bun (A$), smoothed using Bill Cleveland’s locally weighted least squares Lowess/Loess algorithm. http://en.wikipedia.org/wiki/Lowess

The relationship shown above is vaguely linear or, rather, ‘monotonic’, at least until I can have a better go with some nonlinear routines.

A simple linear regression model accounts for around 42% of the variation in taste, in this small and hardly random sample, returning the equation y=1.71*unitprice+1.98, suggesting (at best) that subjective taste, not necessarily representing anyone in particular, increases by 1.7 with every dollar increase in unit price.

But the fun really begins when looking at the residuals, the difference between the actual taste score, and that predicted using the above model. Some buns had negative residuals, indicating (surprise surprise!) that their taste was (much) lower than expected, given their price. I won’t mention the negatives.

As to the positives, two bakeries, Woodfrog Bakery in St. Kilda (Melbourne, Australia) and Candied Bakery in Spotswood (ditto), both cost $2.70 each and so were predicted to have a taste score out of 10 of 6.6, yet Woodfrog hopped in with an actual score 8.5 and Candied with an actual score of 8.

The results can’t be generalised, prove nothing at all, and mean extremely little, except to suggest that regression residuals can perhaps be put to interesting uses, but please take care in trying this at home! Tread softly and carry a big (regression) book e.g Tabachnick and Fidell’s Using Multivariate Statistics

(or the Manga Guide to Regression, when published! http://www.nostarch.com/mg_regressionanalysis.htm)

Happy Numbers

Why there are so very few statisticians as heroes (or even dashing villains) in novels is a pop culture mystery even bigger than the true identity of reggae magicians Johnny and the Attractions, or the actual final resting place of Butch and Sundance.

I have heard of, but don’t have, the 2008 novel Dancing with Dr Kildare, which features British medical statistician Nina, as well as the Finnish composer Sibelius, and the Tango, by Jane Yardley PhD, in real life a co-ordinator of medical trials for a small pharma.

http://onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2009.00341.x/abstract.

http://www.transworld-publishers.co.uk/catalog/book.htm?command=Search&db=twmain.txt&eqisbndata=0552773107

I’m now performing statistical consulting at two major hospitals so I’m about to re-read that wonderful book by major scriptwriter / drama writer Jim Keeble ‘The Happy Numbers of Julius Miles’, originally published in 2012 by independent outfit Alma

http://www.almabooks.com/the-happy-numbers-of-julius-miles-p-387-book.html

but there seems to be an April 2014 printing for the US.

It’s a great book about a big fellow, Julius Miles, a professional statistician with Barts Health NHS Trust, Royal London Hospital, Whitechapel, East London, England. Julius loves stats – nose-counting ones such as the fact that it takes him 2 minutes to polish his shoes (with 30 seconds airing between polish, application and buff), as well as meaty methods such as multilevel Poisson regression for length of hospital stay.

Julius is about 1.93 metres (6 foot 4 inches) and wears size 13 (UK) shoes, a solid fellow (although not reminiscent of the solid Ignatius Reilly in John Kennedy Toole’s classic posthumous 1980 novel ‘A Confederacy of Dunces’).

There’s something about the name Julius, Julius Sumner Miller the US physicist and educator whose ‘Why is it so?’ ran on Australian TV for over 20 years from the 1960’s, and the frothy US drink Orange Julius, named after Julius Freed, around since 1926, taking off in ’29 (the official drink of the 1964 New York World’s Fair).

I can thoroughly recommend this colourful & warm book about Julius Miles, medical statistician.

When Boogie becomes Woogie, when Dog becomes Wolf

An exciting (and not just for statisticians!) area of application in statistics/analytics/data science relates to change/anomaly/outlier detection, the general notion of outliers (e.g. ‘unlikely’ values) having been covered in a previous post, looking at, amongst other things, very long pregnancies.

But tonight’s fr’instance comes from Fleming’s wonderful James Bond Jamaican adventure novel, Dr No, (also a jazzy 1962 movie) which talks of London Radio Security shutting down radio connections with secret agents, if a change in their message transmitting style is detected. This may have indicated that their radio had fallen into enemy hands.

To use a somewhat less exotic example, imagine someone, probably not James Bond, tenpin bowling and keeping track of their scores, this scenario coming from HJ Harrington et al’s excellent Statistical Analysis Simplified: the Easy-to-Understand Guide to SPC and Data Analysis (McGraw-Hill, 1998).

On the 10th week, the score suddenly drops more than three standard deviations (scatter or variation around the mean or average) below the mean.

Enemy agents? Forgotten bowling shoes? Too many milk shakes?

Once again, an anomaly or change, something often examined in industry (Statistical Process Control (SPC) and related areas) to determine the point at which, in the words of Tom Robbin’s great novel Even Cowgirls Get The Blues, ‘the boogie stopped and the woogie began’.

Sudden changes in operations & processes can happen, and so a usual everyday assembly line (‘dog’) can in milliseconds become the unusual, and possibly even dangerous (‘wolf’), at which point hopefully an alarm goes off and corrective action taken.

The basics of SPC were developed many years ago (and taken to Japan after WW2, a story in itself). Anomaly detection is a fast-growing area. For further experimentation / reading, a recent method based upon calculating the closeness of points to their neighbours is described in John Foreman’s marvellous DataSmart: using Data Science to Transform Information into Insight (Wiley, 2014).

We might want to determine if a credit card has been stolen on the basis of different spending patterns/places, or, to return to the opening example, detect an unauthorised intruder to a computer network (e.g. Clifford Stoll’s trailblazing The Cuckoo’s Egg: Tracking a Spy Through the Maze of Computer Espionage).

Finally, we might just want to figure out just exactly when it was that our bowling performance dropped off!

Telstar, Cortina & the Median Quartile Test: where were you in ’62?

It was 1962, the setting of the iconic 1973 movie American Graffiti, from which comes the subtitle of this post. The Beatles had released Love Me Do, their first single. That year also heard and saw Telstar, the eerie but joyful Claviolined Joe Meek instrumental by the Tornados, celebrating the circling communications private transatlantic television satellite it honoured. The British Ford Cortina, named after an Italian ski-resort saw out the humpty-dumpty rounded Prefects and 50’s Zephyrs, while in the US, the first of 50 beautiful, mysterious and largely lost Chrysler Ghia Turbine cars was driven in Detroit.

Meanwhile, the world of statistics was not to be outdone. Rainald Bauer’s Median Quartile test, an extension of Brown and Mood’s early 50’s Median Test, was published, in German, in 1962. The latter test, still available in statistics packages such as IBM SPSS, SAS and Stata simply compares groups on counts below and above the overall median, providing in the case of two groups, a two by two table.

The Median Quartile Test (MQT), as the name suggests, compares each group on the four quartiles. But the MQT is largely unknown, mainly discussed in books and papers published in, or translated from, German.

The MQT conveys similar information to John Tukey’s boxplot, shows both analysts and their customers and colleagues where the data tend to fall, and provides a test of statistical significance to boot. Does one group show a preponderance of scores in the lower and upper quartiles for example, suggesting in the field of pharma fr’instance, that one group either gets much better or much worse.

A 1967 NASA English translation of the original 1962 Bauer paper is available in the Downloadables section of this site.

Recent Application in Journal of Cell Biology

Click to access 809.full.pdf

Further / Future reading

Bauer RK (1962) Der “Median-Quartile Test”… Metrika, 5, 1-16.

Von Eye A et al (1996) The median quartiles test revisited. Studia Psychologica, 38, 79-84.

Expected Unexpected: Power bands, performance curves, rogue waves and black swans

Many years ago, I had a ride of a Kawasaki 500 Mach III 2-stroke motorcycle, which along with its even more horrendous 750cc version was known as the ‘widow-maker’. It was incredibly fast in a straight line, but if it went around corners at all, the rider had long since fallen (or jumped) off!

It also had a very narrow ‘power band’ http://en.wikipedia.org/wiki/Power_band, in that it would have no real power until about 7,000 revs per minute, and then all of a sudden it would whoop and holler like the proverbial bat out of hell, the front wheel would lift, the rider’s jaw drop, and well, you get the idea! In statistical terms, this was a nonlinear relationship between twisting the throttle and the available power.

A somewhat less dramatic example of a nonlinear effect is the Yerkes-Dodson ‘law’ http://en.wikipedia.org/wiki/Yerkes%E2%80%93Dodson_law, in which optimum task performance is associated with medium levels of arousal (too much arousal = the ‘heebie-jeebies’, too little = ‘half asleep’).

Various simple & esoteric methods for finding global (follows a standard pattern such as a U shape, or upside down U) or local (different parts of the data might be better explained by different models, rather than ‘one size fits all’) relationships exist. A popular ‘local’ method is known as a ‘spline’ after the flexible metal ruler that draftspeople once fitted curves with. The ‘GT’ version, Multivariate Adaptive Regression Splines http://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines. is available in R (itself a little reminiscent of a Mach III cycle at times!), the big-iron ‘1960’s 390 cubic inch Ford Galaxie V8′ of the SAS statistical package and the original, sleek ‘Ferrari V12’ Salford Systems version.

Other nonlinear methods are available http://en.wikipedia.org/wiki/Loess_curve, but the thing to remember is that life doesn’t always fit within the lines, or follow some human’s idea of a ‘natural law’.

For example, freak or rogue waves, that can literally break supertankers in half, were observed for centuries by mariners but are only recently accepted by shore-bound scientists, similarly the black swans (actually native to Australia) of the stock market http://www.fooledbyrandomness.com/

When analysing data, fitting models, (or riding motorcycles), please be careful!

Statistical Outliers: of Baldness and Long Gestations

At what point is a human gestation period ‘impossibly’ long. This was the question a British court had to consider in the 1949 appeal to the 1948 judgement in Hadlum vs Hadlum.

Ms Hadlum had a gestation period of 349 days, taking into account when Mr Hadlum went off to the war. The average human gestation is 40 weeks or 280 days, although new research shows an average of 268 days or 38 weeks, varying by +- 37 days http://www.sciencedaily.com/releases/2013/08/130806203327.htm

The widely used statistical definition of an outlier was given by Douglas Hawkins in 1980, ‘an observation which deviates so much from other observations as to cause suspicions that it was generated by a different mechanism’. (Hawkins DM, 1980, Identification of outliers. Chapman & Hall).

Hmn! The court upheld the 1948 finding that such a long gestation was possible, and so Ms Hadlum had not been ‘unfaithful’ to Mr Hadlum, cause for divorce back in those dark days. In the 1951 case of Preston-Jones vs Preston-Jones, however, the court found a gestation period of 360 days to be the limit. The judge concluded that ‘If a line has to be drawn I think it should be drawn so as to allow an ample and generous margin’.

Statisticians have established guidelines for ‘outliers’, that are lines in the sand, if not in concrete.

But speaking of sand, at what point do grains of sand form a heap of sand?

How many hairs constitutes the threshold distinguishing between bald and not bald?

(philosophers call this is the Sorites or ‘heap’ paradox).

The world ‘forgot’ how to make concrete from about 500-1300 AD, but was there a day when we could still make concrete, and a day in which we couldn’t? Something to think about on a Sunday afternoon!

2014 Excel implementation of some simple outlier detection techniques, by John Foreman http://au.wiley.com/WileyCDA/WileyTitle/productCd-111866146X.html

References on the above legal cases

1978 Statistics journal: http://www.jstor.org/discover/10.2307/2347159?uid=2&uid=4&sid=21103476515283

1953 Medical journal: http://link.springer.com/article/10.1007/BF02949756

Olden goldies: Cybernetic forests 1967

Richard Brautigan was an American author and poet who, in 1967’s Summer of Love in San Francisco, published ‘All Watched Over by Machines of Loving Grace’, wishing for a future in which computers could save humans from drudgery, (such as performing statistical operations by hand?)

Apart from the perkier PDP ‘mini-computers’, computers of Brautigan’s day were hulking behemoths with more brawn than brain, and a scary dark side, as seen through HAL in 1968’s ‘2001: The Space Odyssey’.

Brautigan’s poem applied sweet 1960’s kandy-green hues to these cold & clanging monsters, just a few years away from friendly little Apple and other micro’s of the 70’s & 80’s. Now we are all linked on the web, and if we get tired of that we can talk to the electronic aide and confidante Siri, developed at Menlo Park, California – not too far away in space, if not time, from where Brautigan wrote.

We can get a glimpse of a ‘rosy’ future in the even friendlier electronic personality operating system of Spike Jonze’s new movie ‘Her’.

In his BBC documentaries of 2011, named after Brautigan’s poem, filmmaker Adam Curtis argues that computers have not liberated humanity much, if at all.

Yet there is still something about Richard Brautigan’s original 1967 poem, something still worth wishing for!

All three verses, further information and audio of Mr Brautigan reading his poem can be found at

http://www.brautigan.net/machines.html

CSIRAC, a real-life Australian digital dinosaur, that stomped the earth from 1949 to 1964, and is the only intact but dormant (hopefully!) first generation computer left anywhere in the world, can be viewed on the lower ground floor of the Melbourne Museum.

(and yes, this computer was used for statistical analyses, as well as other activities, such as composing electronic music)

http://museumvictoria.com.au/csirac/