women_math {GGMnonreg}

Data: Women and Mathematics

Description

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

Usage

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. doi: 10.1080/01621459.1988.10478640, https://doi.org/10.1080/01621459.1988.10478640.

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. doi: 10.1080/10618600.2012.679239, https://doi.org/10.1080/10618600.2012.679239.

Tarantola C (2004). “MCMC model determination for discrete graphical models.” Statistical Modelling, 4(1), 39–61. doi: 10.1191/1471082x04st063oa, https://doi.org/10.1191/1471082x04st063oa.

Examples

data("women_math")