Stats: The pros and cons of control charts
versus data mining (November 17, 2007). In a talk I gave in December
2006, I highlighted how in the analysis of adverse event data, control charts
can augment more complex statistical tools like data mining. Here's a summary
of the pros and cons of using control charts.
Stats: Common sources of
confusion in a class on quality control (August 22, 2007). I presented a
training class, Stats #18: Quality Control: A Hands-On Workshop, for the
American Society for Andrology. The major emphasis was on the computation of
control charts. It's always interesting to see how this material goes over.
Stats: Calculating a P control
chart (March 7, 2007). If you are collecting data on proportions with a
consistent denominator for each proportion, then you can plot this data on a
control chart. This type of chart is called a P chart and it is very simple
to calculate.
Stats: P control chart,
answers to on your own exercises (March 7, 2007). On the web page Stats:
Calculating a P control chart (March 7, 2007) you were asked to calculate P
charts for two data sets.
Stats: Calculating an XBAR-S
control chart (March 2, 2007). The following data represents a weekly
evaluation of vaccine potency. The data is taken from An adaptation of
quality control chart methods to bacterial vaccine potency testing. H. C.
Batson, M. Brown, M. Oberstein. J Bacteriol 1951: 61(4); 407-19, but I have
taken some liberties with the data to simplify the calculations. Each week,
three lots of vaccine are tested for potency. Calculate a control chart for
this data.
Stats: XBAR-S control
chart, answers to on your own exercise (March 2, 2007). On the web page
Stats: Calculating an XBAR-S control chart (March 2, 2007) you were asked to
calculate an XBAR-S control chart.
Stats: When is a control chart not
a control chart? (February 6, 2007). I found a pair of data sets on the
web that represent counts and where one goal of the data collection is to see
if any of the individual counts differ from the overall average. They look
quite similar and you might be tempted to analyze both of them using a
control chart. But the second example is different in subtle, but important
ways and it is better analyzed using an approach called Analysis of Means (ANOM).
Stats: Unusual advice about
control charts (December 18, 2006). Someone sent me some recommended
guidance on how to use a control chart and it included the following quote: "Do
not correct the process if the out-of-control values can be shown to be due
to chance failure when process is actually in control (special cause
variation)." I'm probably taking this quote out of context, but it is a
rather unusual claim.
Stats: Applications of the CUSUM
chart (June 20, 2006). I am interested in investigating the use of CUSUM
charts in monitoring accrual rates, drop out rates, and adverse event rates
in a clinical trial. Some references which I might cite in a literature
review are listed here.
Stats: Learning more about
control charts (February 1, 2006). Someone asked me about resources for
learning how to use control charts.
Stats: Sigma in the control chart (January 27,
2000). Dear Professor Mean: I ran a control chart in SPSS for
individual values, and the control limits don't correspond with what I would
expect from the descriptive procedure that I ran first. In particular, the
value of sigma in the control chart appears to be an approximation of what I
computed earlier. Why would SPSS use a different calculation for sigma?
This webpage was written on 2007-06-11 and was last modified on
2008-10-06.
