Statistical Science
Опубликовано на портале: 03-10-2003
Harry V. Roberts
Statistical Science.
1990.
Vol. 5.
No. 4.
P. 372-390.
Statistical methodology has great potential for useful application in business, but
that potential is seldom realized. However, companies are increasingly exploiting
simple statistical tools in quality and productivity improvement and developing "company
cultures" congenial to effective use of statistics. Statistical and probabilistic
thinking is essential for sound decision-making. Only with understanding of statistical
variability can managers distinguish special from common causes of variation, intelligently
direct efforts to improve processes, and avoid the tampering that can make processes
worse. Statistics can be used most effectively in business when many employees--"parastatisticians"--have
some grasp of statistical tools and thinking. Fortunately, there is evidence that
very elementary tools suffice to make rough-and-ready studies that can illuminate
most business problems and facilitate most decisions.
Опубликовано на портале: 19-07-2004
David Freedman, Kenneth W. Wachter
Statistical Science.
1994.
Vol. 9.
No. 4 .
P. 476-485.
Current techniques for census adjustment involve the "synthetic assumption" that
undercount rates are constant within "post-strata" across geographical areas. A poststratum
is a subgroup of people with given demographic characteristics; poststrata are chosen
to minimize heterogeneity in undercount rates. This paper will use 1990 census data
to assess the synthetic assumption. We find that heterogeneity within poststrata
is quite large, with a corresponding impact on local undercount rates estimated by
the synthetic method. Thus, any comparison of error rates between the census and
adjusted counts should take heterogeneity into account.
Опубликовано на портале: 19-10-2004
David Banks
Statistical Science.
1993.
Vol. 8.
No. 4..
P. 356-377.
This paper is a critique of the current direction of industrial statistics in the
U.S. The sweep includes not just the toolkit of statistical methods most often employed
in industry, but also international competitiveness, the corporate climate in which
statistical solutions are sought and the educational process which trains applied
statisticians. Much is found that is good, but bland endorsement does little to advance
a field. Therefore most of the paper is deliberately iconoclastic. Almost all of
the extreme viewpoints are rooted in the author's direct experience, working with
statisticians and managers across a range of industries. Some of the perspective
must be attributed to knowledgable gossip, from friends and students who are now
employed by various companies. Incidentally, the paper reviews four new statistical
textbooks for engineers and an edited volume of papers on experimental design for
industry.
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