The reported height and height datasets were collected from three classes taught in the Departments of Computer Science and Biostatistics, as well as remotely through the Extension School. The Biostatistics class was taught in 2016 along with an online version offered by the Extension School. On 2016-01-25 at 8:15 AM, during one of the lectures, the instructors asked students to fill in the sex and height questionnaire that populated the reported height dataset. The online students filled the survey during the next few days, after the lecture was posted online. We can use this insight to define a variable, call it type, to denote the type of student: inclass or online: The code below sets up the dataset for you to analyze in the following exercises:
library(dslabs)
library(dplyr)
library(lubridate)
data("reported heights")
dat mutate(reported-heights, date-time ynd hms(time-stamp)) %)% <. filter(date-time )s make-date(2016, θ1, 25) & date-time < make-date(2016, θ2, 1)) %)% mutateltype İfelse(day(date-time)-= 25 & hour(date-time)-= 8 & between(minute(date-time), 15, 30),
"inclass","online")) %)%
select (sex, type)
y <-factor <- dat$sex, c("Female", "Male"))
x <-dat$type
In the course videos, height cutoffs were used to predict sex. Instead of using height, use the type variable and report your prediction accuracy Use what you learned about Q1 to make an informed guess about sex based on the most prevalent sex for each type. You do not need to split the data into training and test sets.
Write a line of code using the table function to show the confusion matrix between y_hat and y Use the format function(a, b) for your answer and do not name the columns and rows.