18 ◾ Simple Statistical Methods for Software Engineering
If the total number of the numbers (n) is an even number, then the formula is
as follows:
Median
term term
th th
=
+ +
n n
2 2
1
2
Mean
Mean is the arithmetic average of all data points. is is an expression of central
tendency. is is also a parameter to normal distribution:
x
x
n
=
∑
Kurtosis (Flatness of Distribution)
Kurtosis is how peaked the data distribution is. Positive kurtosis indicates a rela-
tively peaked distribution. Negative kurtosis indicates a relatively at distribution
(see Chapter 3 for the formula).
Skewness (Skew of Distribution)
Skewness is a measure of asymmetry in data. Positive skewness indicates a distri-
bution with an asymmetric tail extending toward more positive values. Negative
skewness indicates a distribution with an asymmetric tail extending toward more
negative values (see Chapter 3 for the formula).
References
1. W. Goethert and J. Siviy, Applications of the Indicator Template for Measurement and
Analysis, SEI Technical Note CMU/SEI-2004-TN-024, 2004.
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Guidebook, SEI Handbook CMU/SEI-96-HB-002, 1996.
3. S. S. Stevens, On the theory of scales of measurement, Science, 103, 677–680, 1946.
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1932.
5. F. F. Reichheld, e One Number You Need To Grow, Harvard Business Review,
December 2003.
6. J. Han and M. Kamber, Data Mining—Concepts and Techniques, Morgan Kaupmann
Publishers, 2nd Edition, 2006.
Data, Data Quality, and Descriptive Statistics ◾ 19
7. C. Jones, Programming Productivity, McGraw-Hill Series, New York, 1986.
8. J. A. Park and Y. S. Kim, Visual reasoning and design processes, International Conference
on Engineering Design, 2007.
9. Z. Liu and T. J. Stasko, Mental models, visual reasoning and interaction in infor-
mation visualization: A top-down perspective, IEEE Transactions on Visualization and
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Suggested Readings
Aczel, A. D. and J. Sounderpandian, Complete Business Statistic, McGraw-Hill, London,
2008.
Crewson, P., Applied Statistics Handbook, Version 1.2, AcaStat Software, 2006.
Downey, A. B., ink Stats Probability and Statistics for Programmers, Version 1.6.0, Green
Tea Press, Needham, MA, 2011.
Dyba, T., V. B. Kampenes and D. I. K. Sjøberg, A systematic review of statistical power in
software, Information and Software Technology, 48, 745–755, 2006.
Gupta, M. K., A. M. Gun and B. Dasgupta, Fundamentals of Statistics, World Press Pvt. Ltd.,
Kolkata, 2008.
Hellerstein, J. M., Quantitative Data Cleaning for Large Databases, EECS Computer Science
Division, UC Berkeley, United Nations Economic Commission for Europe (UNECE),
February 27, 2008. Available at http://db.cs.berkeley.edu/jmh.
Holcomb, Z. C., Fundamentals of Descriptive Statistics, Pyrczak Publishing, 1998.
Lussier, R. N., Basic Descriptive Statistics for Decision Making, e-document.
NIST/SEMATECH, Engineering Statistics Handbook, 2003. Available at http://www.itl
.nist.gov /div898/handbook/.
Shore, J. H., Basic Statistics for Trainers, American Society for Training & Development,
Alexandria, VA, 2009. Available at http://my.safaribooksonline.com/book/statistics
/9781562865986.
Succi, G., M. Stefanovic and W. Pedrycz, Advanced Statistical Models for Software Data,
Department of Electrical and Computer Engineering, University of Alberta, Edmonton,
AB, Canada. Proceedings of the 5th World Multi-Conference on Systemics, Cyber-
netics and Informatics, Orlando, FL, 2001. Available at http://www.inf.unibz
.it/~gsucci/publications/images/advanced statisticalmodelsforsoftwaredata.pdf.
Tebbs, J. M., STAT 110 Introduction to Descriptive Statistics, Department of Statistics, Universityof
South Carolina, 2006. Available at http://www.stat.sc.edu/~tebbs/stat110/fall06notes.pdf.
Torres-Reyna, O., Data Preparation & Descriptive Statistics, Data Consultant. Available at
http://www.princeton.edu/~otorres/DataPrep101.pdf.
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