A data frame containing 1190 observations (n = 1190) and 6 variables (p = 6) measured on the binary scale.

data("women_math")

Format

A data frame containing 1190 observations (n = 1190) and 6 variables (p = 6) measured on the binary scale (Fowlkes et al. 1988) . These data have been analyzed in Tarantola (2004) and in (Madigan and Raftery 1994) . The variable descriptions were copied from (section 5.2 ) (section 5.2, Talhouk et al. 2012)

Details

  • 1 Lecture attendance (attend/did not attend)

  • 2 Gender (male/female)

  • 3 School type (urban/suburban)

  • 4 “I will be needing Mathematics in my future work” (agree/disagree)

  • 5 Subject preference (math/science vs. liberal arts)

  • 6 Future plans (college/job)

References

Fowlkes EB, Freeny AE, Landwehr JM (1988). “Evaluating logistic models for large contingency tables.” Journal of the American Statistical Association, 83(403), 611--622.

Madigan D, Raftery AE (1994). “Model selection and accounting for model uncertainty in graphical models using Occam's window.” Journal of the American Statistical Association, 89(428), 1535--1546.

Talhouk A, Doucet A, Murphy K (2012). “Efficient Bayesian inference for multivariate probit models with sparse inverse correlation matrices.” Journal of Computational and Graphical Statistics, 21(3), 739--757.

Tarantola C (2004). “MCMC model determination for discrete graphical models.” Statistical Modelling, 4(1), 39--61.

Examples

data("women_math")