Question 1:

Part A

dat <- read.csv(“https://raw.githubusercontent.com/tmatis12/datafiles/refs/heads/main/RadDat_IMSE.csv”) data <- dat[dat\(Ordered.to.Complete...Mins >= 0, ] head(data) age<-data[data\)PatientAge>=65,] age mdtime<-age\(Ordered.to.Complete...Mins mdtime IQRmd<-quantile(mdtime,probs = c(0.25, 0.75)) IQRmd hiqr<-age[age\)Ordered.to.Complete…Mins>=IQRmd[1] & age\(Ordered.to.Complete...Mins<=IQRmd[2],] hist(hiqr\)Ordered.to.Complete…Mins,main=‘Patients over 65 Time to Fulfill X-Ray Orders’,xlab=‘age’,ylab=‘time in minutes’,col=‘lightpink’,border=‘purple’) #Based on the plot, the time needed to fulfill X-Ray orders decreases as the patient’s age increases.

Question 2:

med62 <- median(data\(Ordered.to.Complete...Mins[data\)Radiology.Technician == 62]) med65 <- median(data\(Ordered.to.Complete...Mins[data\)Radiology.Technician == 65]) med62 med65 # Technician 65 has a lower median compared to technician 62 in the times to complete the x-ray orders

Question 3:

bp_data <- split(data\(PatientAge, data\)Priority) boxplot(bp_data,main=‘Patient Age and Priority Status’,xlab=‘age’,lab=‘priority’,names=c(‘STAT’,‘Routine’),col=c(‘blue’,‘red’)) # From the box plots the routine priority is given over a wider range of ages 30-70, but STAT is given patients of ages 50-70

Question 4:

Calculate mean and standard deviation for each floor

floor3w<-data[data\(Loc.At.Exam.Complete=="3W",] floor4w<-data[data\)Loc.At.Exam.Complete==“4W”,]

mean3w<-mean(floor3w\(Ordered.to.Complete...Mins) sd3w<-sd(floor3w\)Ordered.to.Complete…Mins)

mean4w<-mean(floor4w\(Ordered.to.Complete...Mins) sd4w<-sd(floor4w\)Ordered.to.Complete…Mins) cat(‘Floor 3W mean:’,mean3w,‘Floor 3W standard deviation:’,sd3w) cat(‘Floor 4W mean:’,mean4w,‘Floor 4W standard deviation:’,sd4w) # Floor 3 fulfills X-Ray orders quicker than Floor 4.