Forty A1c values, a long right tail, and a median with interquartile range defended in writing: that is what the HS345 Unit 2 descriptive statistics exercise example contains. Searches like "hs 345 unit 2 assignment example", "hs345 unit 2 sample" and "hs345 unit 2 example" land here.
What a finished HS345 Unit 2 descriptive statistics exercise looks like
Page one lists the forty values sorted from 6.1 to 13.0 percent, so a reader can check every figure that follows. A summary table sits beneath them: mean 8.29, median 7.75, standard deviation 1.73, first and third quartiles of 7.08 and 9.05, and an interquartile range of 1.98. A histogram with half-point bins and a box plot share one horizontal scale, and the box plot marks two values, 12.4 and 13.0, beyond an upper fence of about 12.0. Two threshold counts follow: 10 of 40 patients above 9.0 percent and 8 below 7.0. One categorical variable, insulin use, appears as a count and a percentage, 14 of 40 or 35 percent. The page closes on a half-page defense of the summary chosen.
How a HS345 Unit 2 example is structured
Everything is ordered so that the choice of summary comes after the evidence for it. The raw values come first because a set of forty is small enough to print, and printing it lets a grader recompute the table. Both centers and both spreads are then reported side by side, with a sentence noting that the mean sits about half a point above the median. The graphs follow the numbers and are read rather than displayed: the right tail, the two outliers and the tight cluster between 7 and 8 percent are each named. Threshold counts come next because a clinic acts on how many patients sit above a line, not on a center. The defense is last. It recommends the median and interquartile range for the director, keeps the mean for anyone pooling clinics, and explains the reason for each.
Forty values in order
The sorted list, the date window of the tests, and a note that each patient contributes only a most recent result, so nobody is counted twice.
Two centers half a point apart
Mean 8.29 against median 7.75. The exercise traces the gap to a handful of high values and shows that setting aside the two outliers moves the mean to about 8.06 while the median barely shifts, to 7.65.
A box plot with two marked points
Quartiles by the inclusive method, fences at 1.5 interquartile ranges, and a sentence noting that other quartile methods give slightly different cut points without changing the story.
Counts against clinical lines
Ten patients above 9.0 percent, the threshold common diabetes quality measures use for poor control, and eight below 7.0. Each count carries its percentage and its denominator.
Why the director gets the median
The defense names the reader, the shape and the purpose, then states what the median gives up: it will not register improvement among the highest patients unless they cross the middle.
Where marks go in HS345 Unit 2
What earns credit on a descriptive exercise at this point in HS345 is usually calculation accuracy, graph quality, the match between summary and distribution, and the written justification. The justification weighs more than its length suggests, since it alone shows judgment. Accuracy credit needs consistent rounding and a stated quartile method. Graph credit needs a labeled axis in percent units and bins narrow enough to show the tail. Matching credit comes from choosing median and interquartile range for a right-skewed set and saying why the standard deviation, easiest to read when a distribution is roughly symmetric, is reported but not led with. Deductions cluster around outliers deleted without comment, a mean reported alone for skewed data, histograms drawn with gaps between the bars, and a defense that calls the median better without naming for whom.
Get a 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.
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.