Statistical Rethinking 2026: Lecture A08
Solutions to the homework assignment.
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R '%3e%3cg%20transform='matrix(2.62,0,0,2.62,5476.682446,2950.142713)'%3e%3cg%20transform='matrix(1,0,0,1,-50,-50)'%3e%3cg%20transform='matrix(4.166667,0,0,4.166667,0,0)'%3e%3cpath%20d='M9.86,2C8.291,2%207,3.291%207,4.86L7,6.54L11.29,6.54C11.68,6.54%2012,7.11%2012,7.5L4.86,7.5C3.291,7.5%202,8.791%202,10.36L2,14.141C2,15.71%203.291,17.001%204.86,17.001L6.04,17.001L6.04,14.321C6.04,14.318%206.04,14.314%206.04,14.311C6.04,12.748%207.327,11.461%208.89,11.461C8.89,11.461%2014.14,11.461%2014.14,11.461C15.72,11.461%2017,10.19%2017,8.61L17,4.86C17,3.291%2015.709,2%2014.14,2L9.86,2ZM9.14,3.61C9.54,3.61%209.86,3.73%209.86,4.32C9.86,4.91%209.54,5.211%209.14,5.211C8.75,5.211%208.43,4.911%208.43,4.321C8.43,3.731%208.75,3.61%209.14,3.61'%20style='fill:rgb(106,106,106);fill-rule:nonzero;'/%3e%3c/g%3e%3cg%20transform='matrix(4.166667,0,0,4.166667,0,0)'%3e%3cpath%20d='M17.959,7L17.959,9.68C17.959,9.683%2017.959,9.686%2017.959,9.689C17.959,11.252%2016.672,12.539%2015.109,12.539C15.109,12.539%209.86,12.539%209.86,12.539C9.857,12.539%209.853,12.539%209.85,12.539C8.287,12.539%207,13.826%207,15.389C7,15.389%207,19.139%207,19.139C7,20.708%208.291,21.999%209.86,21.999L14.14,21.999C15.709,21.999%2016.999,20.709%2017,19.14L17,17.46L12.709,17.46C12.319,17.46%2012,16.89%2012,16.5L19.14,16.5C20.709,16.5%2022,15.209%2022,13.64L22,9.86C22,8.291%2020.709,7%2019.14,7L17.959,7ZM8.32,11.513L8.316,11.517L8.354,11.513L8.32,11.513ZM14.86,18.789C15.25,18.789%2015.57,19.089%2015.57,19.679C15.57,20.068%2015.249,20.389%2014.86,20.389C14.46,20.389%2014.14,20.269%2014.14,19.679C14.14,19.089%2014.46,18.789%2014.86,18.789'%20style='fill:rgb(153,153,153);fill-rule:nonzero;'/%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/svg%3e){kind=small}
Python '%3e%3cg%20transform='matrix(1,0,0,1,4026.114702,2779.781761)'%3e%3cg%20transform='matrix(1,0,0,1,-104.166667,-104.166667)'%3e%3cg%20transform='matrix(4.166667,0,0,4.166667,0.875,-1029.208333)'%3e%3ccircle%20cx='13.497'%20cy='281.63'%20r='9.555'%20style='fill:rgb(140,140,140);'/%3e%3ccircle%20cx='36.081'%20cy='281.63'%20r='9.555'%20style='fill:rgb(102,102,102);'/%3e%3ccircle%20cx='24.722'%20cy='262.39'%20r='9.555'%20style='fill:rgb(64,64,64);'/%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/svg%3e){kind=small}
Julia In this eighth exercise from the beginner section, we’re using ulam and MCMC diagnostics for the first time. We modify the item response model from the lecture to find out wether French and American judges differ in how they rate wine by estimating (total) causal contrast.
Even if you’ve never heard of item response models before, this shouldn’t be especially difficult, because we really just modify what McElreath showed in his lecture.