Logistic regression

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Logistic regression is a statistical technique that allows the prediction of categorical dependent variables on the bases of categorical and/or continuous independent variables (Pallant, 2005; Tabachnick & Fidell, 2007).
Logistic regression assumptions relate to sample size, multicollinearity and outliers.
The output statistics of interest include the:
1. Classification Tables: Used to determine whether prediction has improved across the models
2. Omnibus Tests of Model Coefficients: Which indicates whether the improvement across the models is significant
3. Model Summary: Which indicates the amount of variance accounted for by the models)
4. Variables in the Equation: Which indicates the importance of each of the independent variables within the models:
  1. Wald statistics: The squared ratio of the unstandardized logit coefficient to its standard error.

[edit] References

Brace, N., Keup, R. & Snelgar, R. (2003). An introduction to logistic regression (Section 4). In SPSS for psychologists (2nd. ed.). New York: Palgrave Macmillan.
Mertler, C. A., & Vannatta, R. A. (2002). Logistic regression (Ch 11). In Advanced and multivariate statistical methods: Practical application and interpretation (2nd ed.) (pp. 313-330). Los Angeles: Pyrczak Publishing.
Pallant, J. (2005). Logistic regression (Ch 14). In SPSS survival manual (2nd ed.). Sydney: Allen & Unwin.
Peng, J. C., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96(1), 1-14.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.) Boston, MA: Allyn-Bacon.

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