DATA IN EVERYDAY LIFE
Both sets of data are quantitative and should be organized in a manner that allows for the
generation of relevant inferences. The first set of data comprises of a list of all the injuries that
were seen in a clinic in a month's time, and can be best represented using a cumulative frequency
table. Data organized using a cumulative frequency table. Assuming that the hospital has been
reporting an average number of injuries over the past year, the cumulative frequency would be
very useful in determining the number of injuries that lie above or below the mean value.
The second set contains data on the number of minutes that each patient spent in the waiting
room of a doctor's office, and frequency table would be useful in this case because most patient
wait times will be identical, and a frequency table would provide a good representation of the
data as grouped data, especially along with their corresponding frequencies. A frequency table
will provide an overview of all distinct values and the number of times they occur.
Graphs provide an excellent opportunity for researchers to establish trends in the data collected
(Nolan & Perrett, 2016). To graph the first set of data, a line graph would be very useful in
representing the set of data values in a manner that demonstrates variation in the frequency of the
injuries with time. This means that the x-axis would have the time and the y-axis would have the
number of injuries, thus, providing a general pattern in the data sets as noted by Holmes,
Illowsky, and Dean (2018).
The second set of data can be best represented using a histogram graph because is considered the
best with continuous quantitative data (Holmes et al., 2018). The data (wait times) will be put
into classes or bins with regular intervals and frequency counts. The frequency for each bin
would be graphed on the y-axis while the bins would be on the x-axis.
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