3.
Production
Process Characterization
3.4.
Data Analysis for PPC
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Gather all of the data into one place
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After executing the data collection
plan for the characterization study, the data must be gathered up for analysis.
Depending on the scope of the study, the data may reside in one place or
in many different places. It may be in common factory databases, flat files
on individual computers, or handwritten on run sheets. Whatever the case,
the first step will be to collect all of the data from the various sources
and enter it into a single data file. The most convenient format
for most data analyses is the variables-in-columns format. This format
has the variable names in column headings and the values for the
variables in the rows.
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Perform a quality check on the data using
graphical and numerical techniques
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The next step is to perform a quality check on the data. Here we are
typically looking for data entry problems, unusual data values, missing
data, etc. The two most useful tools for this step are the
scatter plot and the
histogram.
By constructing scatter plots of all of the response variables,
any data entry problems will be easily identified. Histograms of
response variables are also quite useful for identifying data entry problems.
Histograms of explanatory variables help identify problems with the execution
of the sampling plan. If the counts for each level of the explanatory
variables are not the same as called for in the sampling plan, you know
you may have an execution problem. Running numerical summary statistics
on all of the variables (both response and explanatory) also helps to identify
data problems.
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Summarize data by estimating location,
spread and shape
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Once the data quality problems are identified and fixed, we should
estimate the location, spread and shape for all of the response variables.
This is easily done with a combination of histograms and numerical summary
statistics.
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