# Run this code to load the required packages suppressMessages(suppressWarnings(suppressPackageStartupMessages({ library(mosaic) library(supernova) library(Lock5withR) }))) # Adjust the plots to be a bit smaller options(repr.plot.width = 6, repr.plot.height = 4)

CensusSchool <- read.csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vSVaWnM4odSxy0mlnhWvvGbeLtiKoZmsbqC6KLzXtBOjQfrF9EVKuX4RVh3XbP3iw/pub?gid=2100178416&single=true&output=csv", header = TRUE) str(CensusSchool)

gf_point(Paid_Work_Hours ~ Gender, data = CensusSchool)

CensusSchool4 <- filter(CensusSchool, Paid_Work_Hours != 7000, Gender != "NA") gf_jitter(Paid_Work_Hours ~ Gender, data = CensusSchool4)

CensusSchool9 <- filter(CensusSchool, Paid_Work_Hours <= 168) gf_point(Paid_Work_Hours ~ Gender, data = CensusSchool9)

CensusSchool9 <- filter(CensusSchool, Paid_Work_Hours <= 168, Gender != "NA") gf_jitter(Paid_Work_Hours ~ Gender, data = CensusSchool9)%>% gf_boxplot(alpha = .1, color = "red", size = .5)

CensusSchool10 <- filter(CensusSchool, Paid_Work_Hours < 40, Gender != "NA") gf_jitter(Paid_Work_Hours ~ Gender, data = CensusSchool10)%>% gf_boxplot(alpha = .4, color = "dodgerblue2")

censusschool.model <- lm(Paid_Work_Hours ~ Gender, data = CensusSchool10) censusschool.model

Genderpredictions <- predict(censusschool.model) Genderpredictions

gf_jitter(Paid_Work_Hours ~ Gender, data = CensusSchool10) %>% gf_jitter(Genderpredictions ~ Gender, color = "red", size = 0.3)

supernova(censusschool.model)