Mathematics
ABTCC Possible Solutions for Outliers in Production Data Discussion

Asheville Buncombe Technical Community College

### Question Description

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A researcher is measuring production levels in several plants, with the total number of 172 subjects in 5 plants. There are 2 outliers in the very low production area 1 more in the highest production area. Discuss possible reasons for outliers and possible solutions in data analysis. Give 4 rea

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Possible solutions for outliers in production data
Problem Description
A researcher is measuring production levels in several plants, with the total number of 172
subjects in 5 plants. There are 2 outliers in the very low production area and 1 more in the
highest production area. Discuss possible reasons for outliers and possible solutions in data
analysis. Give 4 reasons why outliers exist.

Data Analysis
The sample size of the data is 172, and it contains 3 outliers: 2 in the very low production area
and 1 in the highest production area.
Because the highest production area generates more revenue, the presence of outliers in this area
is of great concern.
Outliers exist in production data for a variety of reasons. To analyze the problem, we should
(a)
What is an outlier?
(b)
Why do outliers occur?
(c)
Why are outliers undesirable?
(d)
What statistical methods could be used to accommodate the presence of outliers?
An outlier is a data point (a measurement that defines the expected value of a parameter such as
weight, length or diameter of a product). When the measurement differs significantly from the
expected value, then the measurement is an outlier. For example, if the length of a nail should be
1 inch with an acceptable error of 0.1 inches , then measured values of 0.75 inches or 1.25
inches are outliers.
Outliers occur because of measurement errors (operator or human errors). Outliers also occur
because of instrument errors (defective instruments or instruments that need to be recalibrated).
Outliers are undesirable because they define products that do not meet production standards and
should be rejected. Outliers therefore cause loss in revenue. If outliers are not rejected, they
could cause customer complaints when unacceptable products are returned, and the company’s
reputation suffers.
Because the median is a better estimator of central tendency, it should be used to verify that
production data set is meeting an expected standard (such as an expected mean). When a
noticeable difference exists between the median and the mean, it could signify the presence of
outliers.

Possible solutions
Possible solution #1
Outliers can arise in a production process when instruments in the production system
malfunction. For example, a temperature monitor that is reading incorrect temperatures or a
pressure monitor that is reading incorrect pressures.
A technician can easily detect such errors and a solution can be found. If the instrument is
defective, it should be replaced or repaired. If the instrument needs recalibration, it should be
performed.
If the outliers disappear, then the problem is solved.
Possible solution #2
Outliers can arise in a production process when an operator (such as a technician working on the
production line) is not performing his/her duties correctly.
If poor performance is the cause of outliers, then the operator should be replaced, retrained or
fired.
Possible solution #3
Compare the median (rather than the mean or average of the data) to the expected mean because
the median is a better estim...

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