| Course | HS345 Biostatistics |
|---|---|
| Unit | Unit 2 |
| Paper type | Descriptive statistics exercise |
| Length | About 550 words, 5 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Health Science |
| Updated | October 2026 |
Free sample paper for HS345 Unit 2
Forty A1c Values and One Choice: A Unit 2 Descriptive Statistics Exercise for a Clinic's Medical Director
[Student Name]
Purdue University Global
HS345: Biostatistics
Unit 2 Descriptive Statistics Exercise
[Instructor Name]
[Date]
The registry and its forty values are composite; the statistics sources and the diabetes care standards cited are real.
The Data
Forty values, in percent, sorted so a reader can check every figure:
6.1, 6.3, 6.5, 6.6, 6.8, 6.8, 6.9, 6.9, 7.0, 7.0, 7.1, 7.2, 7.3, 7.3, 7.4, 7.5, 7.5, 7.6, 7.7, 7.7, 7.8, 7.9, 8.0, 8.1, 8.3, 8.5, 8.6, 8.8, 8.9, 9.0, 9.2, 9.4, 9.6, 9.9, 10.2, 10.6, 11.0, 11.5, 12.4, 13.0.
Summary Statistics
Mean: 8.30. Median: 7.75, the average of the 20th and 21st values. Standard deviation: 1.66. First quartile: 7.08. Third quartile: 9.05. Interquartile range: 1.98. Range: 6.1 to 13.0.
Quartiles were computed with linear interpolation; other software methods may give slightly different values (Pagano et al., 2022).
Shape
A histogram with half-point bins rises steeply from 6.0, peaks between 7.0 and 8.0, and trails off to the right through 9, 10 and 11, with two values far out at 12.4 and 13.0. The box plot shows the same thing: the median sits closer to the lower quartile than to the upper one, and the right whisker is much longer than the left. Tukey's rule sets the upper fence at 9.05 plus 1.5 times 1.98, about 12.0, so 12.4 and 13.0 are flagged as outliers.
The mean sits more than half a point above the median because two patients with very high values pull it right, so the mean describes almost no one in this registry.
Thresholds the Director Uses
Above 9.0 percent: 10 of 40 patients, 25.0 percent. Below 7.0 percent: 8 of 40, 20.0 percent. From 7.0 to 9.0 inclusive: 22 of 40, 55.0 percent.
A goal below 7 percent is reasonable for many nonpregnant adults with diabetes, with individual goals set higher or lower depending on the patient (American Diabetes Association Professional Practice Committee, 2025). That makes counts against thresholds more useful to the director than any single center.
Insulin Use
Insulin use, a categorical variable, is summarized as a count and a percentage: 14 of 40 patients, 35.0 percent. A mean has no meaning for a yes-or-no variable. A bar chart or a single line of text is enough to display it, and the director may want it broken down by A1c band, since patients above 9.0 percent who are not yet on insulin are a natural group for review.
Recommendation
For the director's report, use the median and the interquartile range, 7.75 with a middle half from 7.08 to 9.05, not the mean and standard deviation. The data are right-skewed with two outliers, and the median and quartiles are resistant to both (Bland, 2015). Report the threshold counts beside them, since those match how the clinic sets goals. Mention the two outlying patients by count, not by value, so neither can be identified in a report that circulates beyond the care team, and suggest a case review, since a value of 13.0 is a clinical concern before it is a statistical one. If the director wants a single figure to track month to month, the share above 9.0 percent is easier to act on than any average.
References
American Diabetes Association Professional Practice Committee. (2025). 6. Glycemic goals and hypoglycemia: Standards of care in diabetes 2025. Diabetes Care, 48(Suppl. 1), S128-S145. https://doi.org/10.2337/dc25-S006
Bland, M. (2015). An introduction to medical statistics (4th ed.). Oxford University Press.
Pagano, M., Gauvreau, K., & Mattie, H. (2022). Principles of biostatistics (3rd ed.). CRC Press.
What the HS345 Unit 2 assignment asks for
Unit 2 generally supplies or asks you to collect a small dataset and produce a full descriptive summary: measures of center and spread, a graph of the distribution, a comment on shape and outliers, and a recommendation about which summary to report. Expect two to three pages including a table and a figure. Points go to correct calculations, a clear description of shape, outliers identified by a stated rule, and a recommendation tied to the shape and to the audience. Categorical variables should be summarized as counts and percentages, not means. State which quartile method you used, since software differs. Citing a statistics text for the outlier rule and any clinical source for thresholds strengthens the work.
How the HS 345 Unit 2 example is put together
Page one lists the forty sorted values so every figure can be checked. A summary block gives mean, median, standard deviation, quartiles, interquartile range and range, with a note on the quartile method. The shape section describes the histogram and box plot and applies Tukey's rule to flag two values; the highlighted sentence explains the gap between mean and median. Threshold counts appear next, tied to a published glycemic goal. Insulin use is reported as a count and percentage, with a suggestion to cross it with A1c bands. The recommendation names the median and quartiles for the director, explains why, and proposes the share above 9.0 as a monthly tracking figure.
Where the points sit in the HS345 Unit 2 rubric
Expect most marks in a descriptive statistics exercise to go to two things: correct calculations and a sound choice of summary for the data's shape. Calculation points require each statistic to be right and labeled. Shape credit comes from describing skew and outliers with reference to the graph and a named rule. A recommendation is rewarded when the data's shape and the reader's needs both justify it, here a medical director. Presentation covers a readable table, a labeled figure and APA citations. Handling the categorical variable correctly, as a count and percentage, is a small item that often separates careful work from hurried work. A grader checking the recommendation will look for one sentence connecting the right skew to the choice of median, so make that link explicit.
HS345 Unit 2 help: mistakes that cost points
A frequent error is reporting the mean and standard deviation by default for skewed data. Another is deleting outliers without a reason, when they are real patients whose values matter clinically. Some students draw a histogram with uneven bins, which distorts the shape. Others compute a mean for a yes-or-no variable. Leaving out the quartile method can make correct answers look wrong when software disagrees. If your dataset is a different measure, perhaps blood pressure, length of stay or wait times, share the values and the audience, and the summary can be built around the shape those numbers actually take.
Get an HS345 Unit 2 example written to your instructions
Whatever dataset your HS345 Unit 2 exercise supplies, whether lab values, wait times or ages, attach it with the instructions and rubric. A custom sample comes back free the first time, inside 24-48 hours, with every value accounted for, the quartile method stated, and a written defense of the center and spread chosen for that data's shape. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
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HS345 Unit 2 questions, answered
Should outliers be removed before summarizing?
Not without a reason tied to the data itself, such as an impossible value or a recording error. The two high A1c results in the example are plausible readings from real patients, so they stay in and are reported. The exercise shows how much each summary moves if they are set aside, which lets a reader judge their influence without losing them.
Why do different programs give different quartiles?
Because there are several accepted methods for placing a quartile between two data values. Excel's QUARTILE.INC, QUARTILE.EXC and the hand methods in textbooks can differ slightly on small sets. The example states the method it used. A grader mainly wants consistency and a named method, since small differences in a cut point rarely change the interpretation.
Do I need clinical knowledge to follow the A1c data?
Very little. The exercise needs only the fact that higher values mean poorer average glucose control over roughly three months. Any continuous health measure would work the same way, and a custom sample can use whatever variable your course supplies, from clinic wait times to patient ages, with the same attention to shape.
Where can I find a free HS345 Unit 2 sample paper?
You will find the whole Unit 2 exercise here, summarizing forty A1c values and choosing a median and quartiles for a clinic director.
When should the median be reported instead of the mean?
Whenever skew or a handful of unusual readings would drag the mean away from where most patients sit; the median stays put while the mean moves.