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The Future of Statistical Software Proceedings of a Forum (1991) / Chapter Skim
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Morning Discussion
Pages 23-32

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From page 23...
... After five or six years of dealing with military simulations for our designed experiments, where ~ had people run multivariance regression models in two weeks and analyze the nice multivariance regression model, I'm now in a setting where all of the data is serendipitous. We held a seminar yesterday in which many attendees asked whether they could assume that a data set from some company was part of the universe of employee records.
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From page 24...
... There are many complex issues involved in doing that. However, we can put a plot up and look at residuals for our data sets that are generated in the psychology laboratory or in many of the non-serendipitous database contexts.
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From page 25...
... In fact, with sufficiently large data sets, almost any plotting technique will break down in a sense, because the result is a totally black page; every single point is darkened. PAUL TUKEY: See Dan Carr about that.
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From page 26...
... It is definitely targeted at people such as myself. Yet, I came very close to a major statistical analysis disaster.
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From page 27...
... HERBERT EBER (Psychological Resources, Inc.) : The answer to the questions of what to do with huge data sets, and with the fact that everything is then significant, has been around for a while.
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From page 28...
... There was a mention of providing test data sets. As a comparison, I think that test data sets are inevitably a failure, because the result is programs that get the right answer on the test data sets, but not necessarily 28
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From page 29...
... WILLIAM EDDY: ~ want to make a comment about standard algorithms versus standard data sets. If you read the IEEE floating-point arithmetic standard 754, you will see that it does not specify how to do the arithmetic.
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From page 30...
... KEITH MULLER: Concerning the issue of large data sets that several members of the audience raised earlier, I presented a paper with some co-authors a few years ago [Muller, et al., 1982] in which we talked about the analysis of "not small" data sets.
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From page 31...
... CLIFTON BAILEY: If we had analog data on all of the Medicare patients like that which is collected at bedside, our data sets would be very, very small. DANIEL CARR (George Mason University)
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From page 32...
... 3, 421-449. Efron, Bradley, and Robert Tibshirani, 1991, Statistical data analysis in the computer age, Science, Vol.
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Key Terms

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  • design experiments
  • random sample

  • standard algorithms
  • serendipitous data
  • standard statistical
  • test data
  • people run

  • statistical computing
  • equipment standard
  • experimental design
  • software available
  • help people

  • analysis management
  • multivariance regression
  • statistical issue
  • clifton bailey
  • major statistical

  • exploratory methods
  • standard data
  • simple random
  • lag time
  • providing test

  • run multivariance
  • funding interdisciplinary
  • computer age
  • design tends
  • nice multivariance


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