We went over the "Forecasting" chapter, which makes heavy use of moving averages and regression. I would also like you to review the attached paper and provide any comments or real life sagas with such misuse of statistical principles.

See http://elsmar.com/Forums/showthread.php?t=9081 for the paper.

Worth +1 point extra credit on the final.

Interesting how the same numbers can be manipulated to tell the story most adventageous at the time. A truly ethical and skilled manager should have the gumption to accept reality however difficult it is to accept but I know that is not typically the case.

Numbers can be manipulated as well as the written word. Take for instance a resume, written skillfully to include all the right key words. Not necessarily outright lies, but not exactly true either. "Experienced with excel, powerpoint and access" sounds great but what does "experienced" really encompass? One year, one month, one week of experience...it is hard to tell. Experience is experience but how useful is this information at face value?

My thoughts are our jobs as Managers are to lead by example; be truthful, ethical and honest is all our dealings. Stand up when it is time to stand firm, compromise when it is time to compromise and the wisdom to know what to do when.

Lori Beeler
MBA Student

Benjamin Disraeli , the prime minister of the British Empire from 1874-1880, was reported by Mark Twain to have uttered this brilliant quote on statistical analysis: “There are three kinds of lies: lies, d*** lies, and statistics.”
-Icy

Benjamin Disraeli , the prime minister of the British Empire from 1874-1880, was reported by Mark Twain to have uttered this brilliant quote on statistical analysis: “There are three kinds of lies: lies, d*** lies, and statistics.”
-Icy

Icy,

This is one of the very few times I've ever seen this quote properly attributed. The attribution usually goes to Twain (who did say it) or Disraeli (there doesn't appear to be a reliable primary source) but hardly anyone reports it as Twain quoting Disraeli, which is more accurate, of course.

I hope your MBA students are listening to you, Steve. Before I got into quality, I did budgeting and strategic planning that involved a lot of forecasting. Since I had an MBA, I used many of the techniques that are in your chapter. Even worse, I sometimes reset my "target" forecast based on the actual versus forecast. My forecasts started showing more and more variation from actual when I made this adjustment. If Steve has not shown you the funnel experiment yet, it will explain what happened to my forecasts.

I finally realized that by forecasting the average with some adjustments for known variation (e.g. new customers), I had better forecasts. Later when I learned SPC, I realized I should have done a control chart and used the average as my forecast with the control limits showing the most likely highest and lowest forecast. For teaching purposes, text books normally show examples where there is a steady increase or decrease in the data. Unfortunately, much of the business world data that the students will see is more like Steve's example.

Bill Pflanz

I worked in Occupational Health for many years and had to keep records on all employee injuries, and turn in the injury numbers to OSHA at the end of every year. When I requested some data re. national injury trends and averages for the previous few years, (from an OSHA administrator at the federal level), I was told that, "He doesn't know where to locate such data."
There were wide variances in national injury numbers (stats) cited, depending upon which journal article(s) I read. Since, all lost-worktime injuries had to be reported to the federal Occupational Safety and Health Administration (OSHA), I thought one of their administrators would be able to send me some data on national averages and trends; HA!
Anita

Benjamin Disraeli , the prime minister of the British Empire from 1874-1880, was reported by Mark Twain to have uttered this brilliant quote on statistical analysis: “There are three kinds of lies: lies, d*** lies, and statistics.”
-Icy
Unfortunately the BBC misattributes the quote but they have an interesting page here on How To Understand Statistics (http://www.bbc.co.uk/dna/h2g2/A1091350)

I totally agree with Lori B. I too find it interesting how numbers (as well as words) can be manipulated to answer an array of questions. Steve has shown in his paper, Liars Figure, and Figures Lie, how data can answer a problem in more than one way. We need to remember this as managers and strive for the truth. A company's growth is dependent on competant forecasting and competant forecasting is dependent the ability to be honest, ethical and truthful in the interpetations of data.

I was particularly struck with the discussion on moving averages. It was so true that by dropping off a bad month and adding a good one, you look great. If the opposite happans, not so good. I have supervisory responsibilities over the Labor and Industries claims within my organization and we try not to look at the moving average. We want to look at a much broader snapshot, otherwise one month of accidents can depress us, even if we were doing great for the previous 11 months! It makes a lot of sense that you really need to study the worth of the data when forcasting for the future.

I really enjoyed Steve's classroom discussion of the forecasting chapter. He has clearly pointed out what so many companies are doing wrong. I guess I am always stuck on the why question. Why do we do something that is known to be inferior. Is it a lack of knowledge or understanding? Is that data easier to manipulate using methods other than control charts or are we just stuck doing what we always do? I suspect that rolling averages are used in the hope of a couple good months rolling off a couple bad months so management can "prove" their latest effort is successful.

We went over the "Forecasting" chapter, which makes heavy use of moving averages and regression. I would also like you to review the attached paper and provide any comments or real life sagas with such misuse of statistical principles.

See http://elsmar.com/Forums/showthread.php?t=9081 for the paper.

Worth +1 point extra credit on the final.

Steve, your paper and our class discussion of forecasting clearly points out the issues and problems with using moving averages. Companies then compound the problem by assigning trendlines to a data presentation that is flawed from the beginning. The importance of tracking and trending data lies in the ability of the analysis to transpose the data to useful, relevant, timely, and accurate information, which can result in knowledge of the system or process being trended and in changes or improvements that can be made to that system or process. This class has been very useful in pointing out to us that we must use a system approach to bring about improvements and changes in our processes to achieve our ultimate goal of meeting and exceeding the customer's expectations. The use of control charts will provide us with a much better data representation, and hopefully will allow us to tranpose that information to improve our processes and to improve our bottom line. As I have said before, we must analyze each critical component of our system from supply of materials to input to our process to transformation of the materials into a product to output to our inventory to our customers. But ultimately, we must have a complete understanding of our customers needs and expectations, and monitor the data points in our system using statistical process control methodologies to achieve success.

We went over the "Forecasting" chapter, which makes heavy use of moving averages and regression. I would also like you to review the attached paper and provide any comments or real life sagas with such misuse of statistical principles.

See http://elsmar.com/Forums/showthread.php?t=9081 for the paper.

I’m a little late chiming in on this one, but I wanted to comment for two reasons. First, I think it’s an excellent article and highlights one of the most important components of what Deming referred to as “Profound Knowledge “: awareness of the power of basic statistical methods. :applause: As Steve points out in the article, some may quibble with the idea of applying normal-curve statistics to (potentially) non-normal distributions, but the basic information that might be derived is likely to be accurate enough to allow prudent decision making, which is what matters in most business cases. If students of business never learn anything else, they need to know how to use the rules of mathematical probability in decision making, and Steve’s article is a very articulate statement of the value of knowledge of basic statistical methods.

The other observation I have is a bit more arcane, but worth mentioning nonetheless. At the end of the piece Steve says that he used Excel functions to generate “random” numbers to form the data set. While in terms of the purpose of the illustration the use of the word “random” is accurate, in actuality it takes much more sophisticated apparatus than a personal computer to generate real random numbers. Most people are surprised to learn that computers are, in general, incapable of true random number generation, and the term pseudorandom came into being to describe what computers (and program algorithms) actually generate.
The way that Steve formed his Excel formula allowed the generation of numbers that were good enough for the demonstration, and as close as Excel can get to actual randomness. In most business cases it will be sufficient.

I won’t go into the bloody details here, but see the link below if you’re interested in learning more. Suffice it to say that if actual randomness is critical to your application, you need to be aware of the limitations of computers and software, and that the unexpected result of using pseudorandom numbers might be unreliable output.

Wikipedia: Pseudorandom number generators (http://en.wikipedia.org/wiki/Pseudorandom_number_generator)

Well stated classmates! It's sad but true that numbers (and resumes') can be skewed to make them look how the author intends. I work with a master spin doctor who can come up with ways to make numbers spin like you wouldn't believe. It's hard to imagine that census data, assessed valuation and salaries can be presented in less than factual ways, but it happens, as we all know. I think the important lesson to learn from this is to verify data and how the person came up with said data.

I thought the paper was very interesting. I have never taken the time to look at how the same data can look different-dependent on how it is graphed. I can see where the moving average would/could be used widely in all industries. This "tool" can make any data look good. I have just started to experience the use of control charts in the laboratory. We use these to watch instrument performance. When there is a sudden drop or rise in the response, I can actually go back to the logbook to see what may have caused this. I plan on comparing these to next years control charts to see if there is a seasonal trend to these environmental samples.

Come on everyone, we see the same thing taking place evey day on the front of the USA Today newspaper. Take a look at whatever the data chart for the day is. There is always the fine print that tells a different story from what you thought you were looking at compared to the headline that goes with the chart.

Nice and very real comments/examples classmates. I like the resume one the best and the question of what does "experience" really mean when it is listed...

Steve,

Great paper. What a scary sense of reality....Guys like you could make or break a business with this stuff. Knowing the data, how it is generated and what story it tells is critical before making a management decision.

Yes, as was pointed out the Excel random number generator is actually a pseudo-random number generator. Computer pseudo-random number generators usually require some sort of "seed" value, then all numbers following are actually deterministic, but appear random. The Excel rand() function has never been documented in the open press, so if you are running a big simulation (per the simulation chapter) you may want to purchase a better quality generator.