Introduction to Linear Regression
A hands-on introduction to Bayesian statistics.
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Stan '%3e%3cg%20transform='matrix(0.350002,0,0,0.350002,5868.906928,2674.234751)'%3e%3cg%20transform='matrix(1,0,0,1,-1508.333333,-1168.75)'%3e%3cg%20transform='matrix(4.166667,0,0,4.166667,0,0)'%3e%3cpath%20d='M361.453,485.937C162.329,485.937%200.906,377.828%200.906,244.469C0.906,111.109%20162.329,3%20361.453,3C560.578,3%20722,111.109%20722,244.469C722,377.828%20560.578,485.937%20361.453,485.937ZM416.641,97.406C265.289,97.406%20142.594,171.314%20142.594,262.484C142.594,353.654%20265.289,427.562%20416.641,427.562C567.992,427.562%20679.687,377.033%20679.687,262.484C679.687,147.971%20567.992,97.406%20416.641,97.406Z'%20style='fill:rgb(140,140,140);'/%3e%3c/g%3e%3cg%20transform='matrix(4.166667,0,0,4.166667,0,0)'%3e%3cpath%20d='M550,377C550,377%20571.822,383.585%20584.5,390C588.899,392.226%20596.51,396.668%20602,402.5C607.378,408.212%20610,414%20610,414L696,559L557,559.062L492,437C492,437%20478.69,414.131%20470.5,407.5C463.668,401.969%20460.755,400%20454,400L420.974,400L421,558.974L298,559.026L298,152.938L545,152.938C545,152.938%20657.5,154.967%20657.5,262C657.5,369.033%20550,377%20550,377ZM496.5,241.024L422.037,240.976L422,310.026L496.5,310.002C496.5,310.002%20531,309.895%20531,274.877C531,239.155%20496.5,241.024%20496.5,241.024Z'%20style='fill:rgb(64,64,64);'/%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/svg%3e){kind=small}
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}
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Julia This is an introduction to Bayesian modelling with the Turing probabilistic programming framework.
We’ll model how weight varies as a function of height and age using the howell1 data set that features prominently in Richard McElreath’s lectures.