6.4 - Lesson 6 Summary

6.4 - Lesson 6 Summary

The logit model represents how a binary (or multinomial) response variable is related to a set of explanatory variables, which can be discrete and/or continuous. In this lesson, we focused on binary logistic regression and categorical predictors, which allowed direct comparisons to the results we saw earlier for two and three-way tabulated data.

We continue the discussion in the next lesson by focusing on continuous predictors. While most of the concepts from classical regression will carry over to the logistic model, there are some additional challenges now. For example, recall how for categorical predictors we were able to group the data into a table with \(N\) rows, where each row represented a unique predictor combination? That may not be possible when the predictor is continuous because every value may be unique, and this will complicate how the goodness-of-fit tests work.

Additional References

  • Collett, D (1991). Analysis of Binary Data.
  • Fey, M. (2002). Measuring a binary response's range of influence in logistic regression. American Statistician, 56, 5-9.
  • Hosmer, D.W. & Lemeshow, S. (1989). Applied Logistic Regression.
  • Fienberg, S.E. The Analysis of Cross-Classified Categorical Data. 2nd ed. Cambridge, MA
  • McCullagh, P. & Nelder, J.A. (1989). Generalized Linear Models. 2nd Ed.
  • Pregibon, D. (1981) Logistic Regression Diagnostics. Annals of Statistics, 9, 705-724.
  • Rice, J. C. (1994). "Logistic regression: An introduction". In B. Thompson, ed., Advances in social science methodology, Vol. 3: 191-245. Greenwich, CT: JAI Press. Popular introduction.
  • SAS Institute (1995). Logistic Regression Examples Using the SAS System, Version 6.
  • Strauss, David (1999). The Many faces of logistic regression. American Statistician.

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