statisticalrethinkin..

290 11. COUNTING AND CLASSIFICATIONproportion admitted0.2 0.4 0.6 0.8ABCDEF2 4 6 8 10 12rowFIGURE 11.5. Predictions for model m1. Blue points and lines are the data,converted to proportions admitted. Lines connect female (circles) and male(triangles) data from the same academic department. Black lines are thepredictions arising from m1’s naive posterior, with 95% HPDI’s shown bythe dashed lines.lines( 1:12 , pred.pr.admit.ci[2,] , lty=2 )Wow, pretty terrible predictions! ere are only two departments in which females hada lower rate of admission than males, and yet the model says that females should expect tohave a 14% lower chance of admission. What has gone wrong here?Hierarchical data structure. Really want a multilevel model, but that will wait a few morechapters. Will use dummy variable (fixed effects) approach for now, assigning dummies toeach department, in order to account for different rates of overall admission among depts.R code11.19# code dummy variables for each departmentd$deptB