--- title: "Couterfeit Notes" output: html_document --- ```{r setup, include=TRUE, message=FALSE} knitr::opts_chunk$set(echo = TRUE) require(Sleuth3) require(tigerstats) ``` Getting the data: ```{r} lines <- "Length Left Right Bottom Top Diagonal Y 214.8 131 131.1 9 9.7 141 0 214.6 129.7 129.7 8.1 9.5 141.7 0 214.8 129.7 129.7 8.7 9.6 142.2 0 214.8 129.7 129.6 7.5 10.4 142 0 215 129.6 129.7 10.4 7.7 141.8 0 215.7 130.8 130.5 9 10.1 141.4 0 215.5 129.5 129.7 7.9 9.6 141.6 0 214.5 129.6 129.2 7.2 10.7 141.7 0 214.9 129.4 129.7 8.2 11 141.9 0 215.2 130.4 130.3 9.2 10 140.7 0 215.3 130.4 130.3 7.9 11.7 141.8 0 215.1 129.5 129.6 7.7 10.5 142.2 0 215.2 130.8 129.6 7.9 10.8 141.4 0 214.7 129.7 129.7 7.7 10.9 141.7 0 215.1 129.9 129.7 7.7 10.8 141.8 0 214.5 129.8 129.8 9.3 8.5 141.6 0 214.6 129.9 130.1 8.2 9.8 141.7 0 215 129.9 129.7 9 9 141.9 0 215.2 129.6 129.6 7.4 11.5 141.5 0 214.7 130.2 129.9 8.6 10 141.9 0 215 129.9 129.3 8.4 10 141.4 0 215.6 130.5 130 8.1 10.3 141.6 0 215.3 130.6 130 8.4 10.8 141.5 0 215.7 130.2 130 8.7 10 141.6 0 215.1 129.7 129.9 7.4 10.8 141.1 0 215.3 130.4 130.4 8 11 142.3 0 215.5 130.2 130.1 8.9 9.8 142.4 0 215.1 130.3 130.3 9.8 9.5 141.9 0 215.1 130 130 7.4 10.5 141.8 0 214.8 129.7 129.3 8.3 9 142 0 215.2 130.1 129.8 7.9 10.7 141.8 0 214.8 129.7 129.7 8.6 9.1 142.3 0 215 130 129.6 7.7 10.5 140.7 0 215.6 130.4 130.1 8.4 10.3 141 0 215.9 130.4 130 8.9 10.6 141.4 0 214.6 130.2 130.2 9.4 9.7 141.8 0 215.5 130.3 130 8.4 9.7 141.8 0 215.3 129.9 129.4 7.9 10 142 0 215.3 130.3 130.1 8.5 9.3 142.1 0 213.9 130.3 129 8.1 9.7 141.3 0 214.4 129.8 129.2 8.9 9.4 142.3 0 214.8 130.1 129.6 8.8 9.9 140.9 0 214.9 129.6 129.4 9.3 9 141.7 0 214.9 130.4 129.7 9 9.8 140.9 0 214.8 129.4 129.1 8.2 10.2 141 0 214.3 129.5 129.4 8.3 10.2 141.8 0 214.8 129.9 129.7 8.3 10.2 141.5 0 214.8 129.9 129.7 7.3 10.9 142 0 214.6 129.7 129.8 7.9 10.3 141.1 0 214.5 129 129.6 7.8 9.8 142 0 214.6 129.8 129.4 7.2 10 141.3 0 215.3 130.6 130 9.5 9.7 141.1 0 214.5 130.1 130 7.8 10.9 140.9 0 215.4 130.2 130.2 7.6 10.9 141.6 0 214.5 129.4 129.5 7.9 10 141.4 0 215.2 129.7 129.4 9.2 9.4 142 0 215.7 130 129.4 9.2 10.4 141.2 0 215 129.6 129.4 8.8 9 141.1 0 215.1 130.1 129.9 7.9 11 141.3 0 215.1 130 129.8 8.2 10.3 141.4 0 215.1 129.6 129.3 8.3 9.9 141.6 0 215.3 129.7 129.4 7.5 10.5 141.5 0 215.4 129.8 129.4 8 10.6 141.5 0 214.5 130 129.5 8 10.8 141.4 0 215 130 129.8 8.6 10.6 141.5 0 215.2 130.6 130 8.8 10.6 140.8 0 214.6 129.5 129.2 7.7 10.3 141.3 0 214.8 129.7 129.3 9.1 9.5 141.5 0 215.1 129.6 129.8 8.6 9.8 141.8 0 214.9 130.2 130.2 8 11.2 139.6 0 213.8 129.8 129.5 8.4 11.1 140.9 0 215.2 129.9 129.5 8.2 10.3 141.4 0 215 129.6 130.2 8.7 10 141.2 0 214.4 129.9 129.6 7.5 10.5 141.8 0 215.2 129.9 129.7 7.2 10.6 142.1 0 214.1 129.6 129.3 7.6 10.7 141.7 0 214.9 129.9 130.1 8.8 10 141.2 0 214.6 129.8 129.4 7.4 10.6 141 0 215.2 130.5 129.8 7.9 10.9 140.9 0 214.6 129.9 129.4 7.9 10 141.8 0 215.1 129.7 129.7 8.6 10.3 140.6 0 214.9 129.8 129.6 7.5 10.3 141 0 215.2 129.7 129.1 9 9.7 141.9 0 215.2 130.1 129.9 7.9 10.8 141.3 0 215.4 130.7 130.2 9 11.1 141.2 0 215.1 129.9 129.6 8.9 10.2 141.5 0 215.2 129.9 129.7 8.7 9.5 141.6 0 215 129.6 129.2 8.4 10.2 142.1 0 214.9 130.3 129.9 7.4 11.2 141.5 0 215 129.9 129.7 8 10.5 142 0 214.7 129.7 129.3 8.6 9.6 141.6 0 215.4 130 129.9 8.5 9.7 141.4 0 214.9 129.4 129.5 8.2 9.9 141.5 0 214.5 129.5 129.3 7.4 10.7 141.5 0 214.7 129.6 129.5 8.3 10 142 0 215.6 129.9 129.9 9 9.5 141.7 0 215 130.4 130.3 9.1 10.2 141.1 0 214.4 129.7 129.5 8 10.3 141.2 0 215.1 130 129.8 9.1 10.2 141.5 0 214.7 130 129.4 7.8 10 141.2 0 214.4 130.1 130.3 9.7 11.7 139.8 1 214.9 130.5 130.2 11 11.5 139.5 1 214.9 130.3 130.1 8.7 11.7 140.2 1 215 130.4 130.6 9.9 10.9 140.3 1 214.7 130.2 130.3 11.8 10.9 139.7 1 215 130.2 130.2 10.6 10.7 139.9 1 215.3 130.3 130.1 9.3 12.1 140.2 1 214.8 130.1 130.4 9.8 11.5 139.9 1 215 130.2 129.9 10 11.9 139.4 1 215.2 130.6 130.8 10.4 11.2 140.3 1 215.2 130.4 130.3 8 11.5 139.2 1 215.1 130.5 130.3 10.6 11.5 140.1 1 215.4 130.7 131.1 9.7 11.8 140.6 1 214.9 130.4 129.9 11.4 11 139.9 1 215.1 130.3 130 10.6 10.8 139.7 1 215.5 130.4 130 8.2 11.2 139.2 1 214.7 130.6 130.1 11.8 10.5 139.8 1 214.7 130.4 130.1 12.1 10.4 139.9 1 214.8 130.5 130.2 11 11 140 1 214.4 130.2 129.9 10.1 12 139.2 1 214.8 130.3 130.4 10.1 12.1 139.6 1 215.1 130.6 130.3 12.3 10.2 139.6 1 215.3 130.8 131.1 11.6 10.6 140.2 1 215.1 130.7 130.4 10.5 11.2 139.7 1 214.7 130.5 130.5 9.9 10.3 140.1 1 214.9 130 130.3 10.2 11.4 139.6 1 215 130.4 130.4 9.4 11.6 140.2 1 215.5 130.7 130.3 10.2 11.8 140 1 215.1 130.2 130.2 10.1 11.3 140.3 1 214.5 130.2 130.6 9.8 12.1 139.9 1 214.3 130.2 130 10.7 10.5 139.8 1 214.5 130.2 129.8 12.3 11.2 139.2 1 214.9 130.5 130.2 10.6 11.5 139.9 1 214.6 130.2 130.4 10.5 11.8 139.7 1 214.2 130 130.2 11 11.2 139.5 1 214.8 130.1 130.1 11.9 11.1 139.5 1 214.6 129.8 130.2 10.7 11.1 139.4 1 214.9 130.7 130.3 9.3 11.2 138.3 1 214.6 130.4 130.4 11.3 10.8 139.8 1 214.5 130.5 130.2 11.8 10.2 139.6 1 214.8 130.2 130.3 10 11.9 139.3 1 214.7 130 129.4 10.2 11 139.2 1 214.6 130.2 130.4 11.2 10.7 139.9 1 215 130.5 130.4 10.6 11.1 139.9 1 214.5 129.8 129.8 11.4 10 139.3 1 214.9 130.6 130.4 11.9 10.5 139.8 1 215 130.5 130.4 11.4 10.7 139.9 1 215.3 130.6 130.3 9.3 11.3 138.1 1 214.7 130.2 130.1 10.7 11 139.4 1 214.9 129.9 130 9.9 12.3 139.4 1 214.9 130.3 129.9 11.9 10.6 139.8 1 214.6 129.9 129.7 11.9 10.1 139 1 214.6 129.7 129.3 10.4 11 139.3 1 214.5 130.1 130.1 12.1 10.3 139.4 1 214.5 130.3 130 11 11.5 139.5 1 215.1 130 130.3 11.6 10.5 139.7 1 214.2 129.7 129.6 10.3 11.4 139.5 1 214.4 130.1 130 11.3 10.7 139.2 1 214.8 130.4 130.6 12.5 10 139.3 1 214.6 130.6 130.1 8.1 12.1 137.9 1 215.6 130.1 129.7 7.4 12.2 138.4 1 214.9 130.5 130.1 9.9 10.2 138.1 1 214.6 130.1 130 11.5 10.6 139.5 1 214.7 130.1 130.2 11.6 10.9 139.1 1 214.3 130.3 130 11.4 10.5 139.8 1 215.1 130.3 130.6 10.3 12 139.7 1 216.3 130.7 130.4 10 10.1 138.8 1 215.6 130.4 130.1 9.6 11.2 138.6 1 214.8 129.9 129.8 9.6 12 139.6 1 214.9 130 129.9 11.4 10.9 139.7 1 213.9 130.7 130.5 8.7 11.5 137.8 1 214.2 130.6 130.4 12 10.2 139.6 1 214.8 130.5 130.3 11.8 10.5 139.4 1 214.8 129.6 130 10.4 11.6 139.2 1 214.8 130.1 130 11.4 10.5 139.6 1 214.9 130.4 130.2 11.9 10.7 139 1 214.3 130.1 130.1 11.6 10.5 139.7 1 214.5 130.4 130 9.9 12 139.6 1 214.8 130.5 130.3 10.2 12.1 139.1 1 214.5 130.2 130.4 8.2 11.8 137.8 1 215 130.4 130.1 11.4 10.7 139.1 1 214.8 130.6 130.6 8 11.4 138.7 1 215 130.5 130.1 11 11.4 139.3 1 214.6 130.5 130.4 10.1 11.4 139.3 1 214.7 130.2 130.1 10.7 11.1 139.5 1 214.7 130.4 130 11.5 10.7 139.4 1 214.5 130.4 130 8 12.2 138.5 1 214.8 130 129.7 11.4 10.6 139.2 1 214.8 129.9 130.2 9.6 11.9 139.4 1 214.6 130.3 130.2 12.7 9.1 139.2 1 215.1 130.2 129.8 10.2 12 139.4 1 215.4 130.5 130.6 8.8 11 138.6 1 214.7 130.3 130.2 10.8 11.1 139.2 1 215 130.5 130.3 9.6 11 138.5 1 214.9 130.3 130.5 11.6 10.6 139.8 1 215 130.4 130.3 9.9 12.1 139.6 1 215.1 130.3 129.9 10.3 11.5 139.7 1 214.8 130.3 130.4 10.6 11.1 140 1 214.7 130.7 130.8 11.2 11.2 139.4 1 214.3 129.9 129.9 10.2 11.5 139.6 1" con <- textConnection(lines) banknotes <- read.csv(con, sep="") banknotes$CLASS <- ifelse(banknotes$Y==1, "GENUINE", "COUNTERFEIT") ``` ```{r} ggplot(banknotes, aes(Bottom, Diagonal)) + geom_point(aes(colour=CLASS)) ``` Let's run Logistic Regression: ```{r} fit <- glm(Y~Bottom+Diagonal, data=banknotes, family=binomial()) summary(fit) ```