Casestudie Breakdown prediction Contell PILOT - Transumo

Inferential statistical methods do not describe available datasets but try to gain
additional information from the existing data. These methods are applied to
problems, where datasets cannot be obtained entirely ([Scharnbacher04], p. 43).
After obtaining parts of the totality, inferential statistical methods are used to
generalize gained results ([Bourier03], p. 3).
Due to the fact that modern computer systems and mainframes are able to compute
large amounts of data within very short time, statistical analysis offers additional
calculation possibilities (e.g. data mining). Furthermore, the ability to collect data in
an automated way increases the data base. As a result, the probability is higher that
the gained generalized results are correct ([Eckey02], p. 3).
5.3 Basic Descriptive Statistical Measures
Section 1.2 defined the two main goals of this diploma thesis. The first one was to
gain additional knowledge of the cooling device’s condition from recorded datasets to
offer additional decision support in case of an exceptional temperature level.
Therefore, a summarization and evaluation of available datasets by using descriptive
statistics appears to be a promising approach. Hence, the succeeding subsections
will introduce as well basic as special descriptive statistical measures from other
already introduced monitoring settings. 45 Moreover, the expected gain of information
is evaluated.
Descriptive statistics offer some very common measures that can be applied easily to
all kinds of numerical data. Most known are: ([Holland01], chapter 4)
• Minimum and Maximum
• The Mode
• The Median
• The Mean
• The Standard Deviation
The first basic descriptive statistical measurements are the minimum and the
maximum value. Their determination can be done with very few time and effort.
Nevertheless, these values can already indicate uncommon behavior. 46
45 See chapter 4 for details
46 See section 6.2.1 for details
58