Advertisement

Enlarging the Model

  • George A. F. Seber
Part of the Springer Series in Statistics book series (SSS)

Abstract

Sometimes after a linear model has been fitted it is realized that more explanatory (x) variables need to be added, as in the following examples.

In an industrial experiment in which the response (y) is the yield and the explanatory variables are temperature, pressure, etc., we may wish to determine what values of the x-variables are needed to produce a certain yield. However, it may be realized that another variable, say concentration, needs to be incorporated in the regression model. This can be readily done by simply using a standard regression computational package. In this case the added variable is quantitative and is readily added into the original model.

Keywords

Usual Model Balance Design Covariance Method Miss Data Point Missing Observation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48, 5–37.CrossRefGoogle Scholar
  2. Bartlett, M. S. (1937). Some examples of statistical methods of research in agriculture and applied biology. Journal of the Royal Statistical Society Supplement, 4, 137–170.CrossRefGoogle Scholar
  3. Graham, J. W. (2012). Missing data: Analysis and design. New York: Springer.CrossRefGoogle Scholar
  4. Kruskal, W. (1960). The coordinate-free approach to Gauss-Markov estimation and its application to missing and extra observations. In Proceedings of the 4th Berkeley symposium in mathematical statistics and probability, Berkeley (Vol. 1, pp. 435–451).Google Scholar
  5. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York: Wiley.zbMATHGoogle Scholar
  6. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592.zbMATHMathSciNetCrossRefGoogle Scholar
  7. Seber, G. A. F., & Lee, A. J. (2003). Linear regression analysis (2nd ed.). New York: Wiley.zbMATHCrossRefGoogle Scholar
  8. Wilkinson, G. N. (1970). A general recursive procedure for analysis of variance. Biometrika, 57, 19–46.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • George A. F. Seber
    • 1
  1. 1.Department of StatisticsThe University of AucklandAucklandNew Zealand

Personalised recommendations