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Statistical Regression Analysis

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Software Project Effort Estimation

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Abstract

In this chapter, we briefly introduce effort estimation based on statistical regression analysis. Regression analysis represents a data-driven, model-based, parametric estimation method that implements the define-your-own-model approach. In other words, in this approach an effort estimation model is created “from scratch” using quantitative project data.

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Notes

  1. 1.

    Chapter 3 provides an overview of the most common effort factors used in the context of software effort estimation.

  2. 2.

    If the functional form is not known and cannot be parameterized in terms of any “basis” function, then methods known as nonparametric regression should be applied. Nonparametric regression constructs regression models according to information derived from the data. Please refer to work of Green and Silverman (1994) for more information on available nonparametric regression analysis techniques.

  3. 3.

    There are a number of imputation techniques available. Please refer to Rubin and Little (1983) for brief introduction to the problem of missing data and to Schafer (1997) for an overview of techniques for dealing with missing data.

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Author information

Authors and Affiliations

  1. Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany

    Adam Trendowicz

  2. The University of New South Wales, Sydney, New South Wales, Australia

    Ross Jeffery

Authors
  1. Adam Trendowicz
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  2. Ross Jeffery
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Further Reading

Further Reading

  • L. Schroeder, D. L. Sjoquist, and P. E. Setphan (1996), Understanding Regression Analysis: An Introductory Guide. Sage Publications, Inc.

    The book provides a basic introduction to regression analysis. It aims at beginners in this topic and provides background knowledge for understanding regression analysis methods and application results presented in the literature.

  • N. R. Draper and H. Smith (1998), Applied Regression Analysis. 3rd Edition. John Wiley & Sons, New York, NY, USA.

    This book provides a comprehensive view on applied regression analysis. It covers linear and parametric regression in detail and provides many other useful references.

  • P.J. Green and B.W. Silverman (1994), Nonparametric Regression and Generalized Linear Models: A roughness penalty approach. 1st Edition. Monographs on Statistics and Applied Probability, Chapman & Hall/CRC.

    This book describes nonparametric regression and Generalized Linear Models.

  • J. Miller, J. Daly, M. Wood, M. Roper, and A. Brooks (1997), “Statistical Power and Its Subcomponents—Missing and Misunderstood Concepts in Empirical Software Engineering Research,” Information and Software Technology, vol. 39, no. 4, pp. 285–295.

    Authors discuss typical misconceptions of power analysis in the context of empirical software engineering.

  • Y. Miyazaki, M. Terakado, K. Ozaki, and H. Nozaki (1994), “Robust Regression for Developing Software Estimation Models,” Journal of Systems and Software, vol. 27, pp. 3–16.

    Authors discuss different types of robust regression analysis for the purpose of developing software effort estimation models. Example analyses include the least-squares of balanced relative errors (LBRS) method that minimizes the sum of squares of balanced relative error or least-squares of inverted balanced relative errors (LIRS) that minimizes the sum of squares of inverted balanced relative error.

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Trendowicz, A., Jeffery, R. (2014). Statistical Regression Analysis. In: Software Project Effort Estimation. Springer, Cham. https://doi.org/10.1007/978-3-319-03629-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-03629-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03628-1

  • Online ISBN: 978-3-319-03629-8

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