Adding missing grouping variables: `.row`
Four Parameters: Categorical
We have taken NES data to understand is the relation between sex and voting in the 1992 presidential election. One concern is that the assumption of representativeness might not be true because the sample of our data is not random enough. We modeled pres_vote, a character variable, as a multinomial logistic regression model. Women are most likely to support Clinton. About 53% of women claim to support Clinton.
\[\begin{aligned} \rho_{clinton} &=& \frac{e^{\beta_{0, clinton} + \beta_{1, clinton} male}}{1 + e^{\beta_{0, clinton} + \beta_{1, clinton} male}}\\ \rho_{perot} &=& \frac{e^{\beta_{0, perot} + \beta_{1, perot} male}}{1 + e^{\beta_{0, perot} + \beta_{1, perot} male}}\\ \rho_{bush} &=& 1 - \rho_{clinton} - \rho_{perot} \end{aligned}\]Characteristic |
Beta |
95% CI 1 |
|---|---|---|
| muClinton_(Intercept) | 0.45 | 0.31, 0.60 |
| muPerot_(Intercept) | -0.86 | -1.1, -0.64 |
| muClinton_sexMale | -0.25 | -0.48, -0.04 |
| muPerot_sexMale | 0.42 | 0.14, 0.71 |
| 1
CI = Credible Interval |
||