A finished BUS-352 Topic 2 distribution shape analysis example, showing how right-skewed order values separate the mean from the median and which figure should anchor a free-shipping threshold. Searches like "bus 352 topic 2 assignment example", "bus352 topic 2 sample" and "bus-352 topic 2 example" land here.
What a finished BUS-352 Topic 2 distribution shape analysis looks like
The finished analysis opens on a histogram, not a table, because the decision depends on where most orders fall. The plot shows a long right tail: most illustrative orders cluster at the low end, while a thin run of large purchases stretches far to the right. The example then reports the median at $38 and the mean at $61, explains that the tail pulls the mean upward, and says which of the two a threshold should be built around. A box plot marks the orders beyond the upper fence, and the example checks whether those belong to business accounts before deciding how to treat them. It closes by recommending a threshold a modest step above the median, noting that one built from the mean would sit above most orders, and naming what to measure after launch.
How a BUS-352 Topic 2 example is structured
The analysis moves from picture to numbers to decision, since the numbers only make sense once the shape is known. It opens with the marketing question and the data behind it: illustrative order values for a recent period, what an order includes, and whether returns were removed. The histogram follows with a sentence describing its shape in plain words, the direction of the skew and where the bulk of orders sits. Summary measures come next, with mean, median and quartiles side by side and the gap between mean and median explained rather than left for the reader to notice. The box plot section identifies the orders beyond the upper fence and investigates them, finding a separate business segment. The analysis then compares candidate thresholds by the share of orders each would sit above. It ends with the recommendation and the post-launch measure.
Shape shown before any average
The histogram comes first, since the decision turns on where most orders cluster, and no single average can show that on its own.
The mean and median gap explained
The illustrative median of $38 and mean of $61 are reported together, and the example attributes the gap to a thin tail of large purchases.
The upper tail traced to its source
Orders past the upper fence are traced to business accounts, which the example treats as a separate segment rather than as errors to discard.
Thresholds compared by share of orders
Each candidate threshold is judged by how many orders already exceed it, which tells marketing how far a typical customer would have to stretch.
A threshold tied to the median
The recommendation sits a modest step above the median and names the post-launch figure, average items per order, that would show whether it worked.
Where marks go in BUS-352 Topic 2
Summarizing skewed data with the mean alone is the error faculty on this topic tend to mark hardest, and here it produces a business consequence: a threshold built from the average order sits above most customers' baskets. A paper that reports the mean and median without saying which one describes a typical customer has left the decision unmade. Describing the histogram's shape backwards is a checkable slip, since a long tail to the right is right skew even though most bars stand on the left. Outliers deleted without investigation remove the business accounts the retailer may most want to understand. Box plots drawn without the fence calculation shown cannot be verified. Recommendations that ignore how customers respond to a threshold, or that promise a revenue gain the data never measured, claim more than a description of past orders supports.
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BUS-352 Topic 2 questions, answered
How do I tell which direction the data are skewed?
Look at where the long tail runs, not where the tallest bars are. Order values bunched at the low end with a few very large purchases stretching right are right-skewed, and in that shape the mean usually sits above the median. If the mean falls below the median, the tail more likely runs left. Stating both the visual reading and the numerical check makes the classification hard to dispute.
Should outliers be removed before summarizing?
Only when there is a reason to believe they are errors, such as a duplicated order or a test transaction. Large legitimate orders are part of the business, and removing them changes the question being answered. The example investigates the orders beyond the fence, finds they come from business accounts, and reports the retail segment separately rather than pretending the large buyers do not exist.
Is the median always the better summary for business data?
No. The median describes a typical order well, but the mean is what multiplies into total revenue, so a finance question about expected sales per thousand orders needs it. The choice follows from what the decision requires. For a threshold aimed at the typical customer, the median fits; for a revenue projection, the mean does, and a strong paper says which question it is answering.