𝕄aximum likelihood estimation is one of the most important tools for statistical inference and is used to find estimates for model parameters. […] Download the slides here […] Check out the FiveMinuteStats series here Read more →

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𝕊ud et al (see reference section). This review covers most of the concepts we want to cover in this class. We will learn by doing and unpack many of the concepts mentioned in the review. Hopefully, you will re-read the paper at the end of the class and realize how much you have learned. Read more →

𝕎e are told that 241,945 girls and 251,527 boys were born in Paris from 1745 to 1770. y = 241945 n = 241945 + 251527 posterior = function(theta) dbeta(theta,shape1 = y+1, shape2 = n- y + 1) curve(posterior,from = 0,to = 1, main=paste("n = ",n,"; y = … Read more →

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𝕟 = 10 y = 8 likefun = function(theta) {theta^y * (1 - theta)^(n-y)} curve(likefun,from = 0,to = 1, main=paste("n = ",n,"; y = ",y),xlab="theta", ylab="likelihood") abline(v=y/n,col='gray') What if we got 10 heads? n = 10 y = 10 likefun = … Read more →

𝕒lpha = 0.05 Patleastonemistake = function(m) {1 - (1-alpha)^m} curve(Patleastonemistake,from = 1, to=100, ylab="Prob at least one wrong", xlab="m = number of tests") grid() abline(h=1,col='gray') Read more →

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𝕝ibrary(tidyverse) ## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ── ## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4 ## ✓ tibble 3.0.5 ✓ dplyr 1.0.3 ## ✓ tidyr 1.1.2 ✓ stringr 1.4.0 ## ✓ readr 1.4.0 ✓ forcats 0.5.0 ## ── Conflicts ────────────────────────────────────────── … Read more →