Final answer:
Your 90-minute ER wait time in the 82nd percentile indicates you waited longer than most patients. Box plots could graphically represent this data. A Poisson distribution could be used to calculate the probability of patient arrivals per hour in an ER.
Step-by-step explanation:
When you are told that your 90-minute wait time in the emergency room is in the 82nd percentile, it means that you waited longer than 82% of all patients. Percentiles are a way to compare scores across a wider range of data and to understand your position relative to others. While being in a higher percentile might be good for academic test scores, in the case of ER wait times, a lower percentile would be more desirable as it would mean a shorter wait time.
Box Plots can be used to visually display the distribution of wait times and show the median, quartiles, and potential outliers. They can aid in understanding how your wait time compares to the collective data.
Epidemiologists often use a sample of patient data to make public health decisions. Analyzing medical records and conducting interviews with patients enables them to identify patterns in healthcare usage, such as unnecessary emergency department visits or recurring health issues.
The probability calculation for the number of patients per hour in an ER is an example of a Poisson distribution, which is used for count data where events (like the arrival of a patient) are independent and occur at a constant average rate.