A finished BUS-352 Topic 6 two sample test report example, comparing two carriers' mean lead times with a Welch t test, a proportion test for on-time rates and a switching recommendation. Searches like "bus 352 topic 6 assignment example", "bus352 topic 6 sample" and "bus-352 topic 6 example" land here.
What a finished BUS-352 Topic 6 two sample test report looks like
The finished report treats the choice of test as part of the answer. It notes that the shipments on each carrier are different shipments, so the samples are independent rather than paired, and that lead time in days is quantitative while delivered-on-time is a yes-or-no outcome needing a different procedure. Illustrative summaries follow: 48 shipments averaging 3.9 days with a standard deviation of 1.1 for the current carrier, 45 averaging 3.4 days with a standard deviation of 1.6 for the challenger. Because the spreads differ, the report uses the unequal-variance version of the t test and says why. The half-day difference does not reach significance at the 0.05 level, and the interval for it runs from slightly below zero to just over a day. The report says plainly that this does not prove the carriers equal.
How a BUS-352 Topic 6 example is structured
The report runs from the design to the decision, with the test chosen before any output appears. It opens with the logistics question, whether switching carriers would shorten lead times, and the two outcomes that bear on it. A design section establishes that the samples are independent, identifies each outcome's data type, and assigns a procedure to each: a comparison of means for days, a comparison of proportions for on-time delivery. The assumptions section examines the lead-time distributions for skew, notes that the sample sizes give the t procedure reasonable protection, and compares the two standard deviations to justify the unequal-variance version. Results follow for both outcomes, each with its test statistic, p-value and interval for the difference. A limits paragraph asks whether routes were assigned to carriers at random. The recommendation closes the report, stated for a logistics manager rather than a statistician.
Independent or paired, decided first
Different shipments went with each carrier, so the samples are independent; matched routes run by both carriers would have called for a paired comparison instead.
One test for days, another for on-time
Lead time is compared with a t procedure and the on-time share with a two-proportion test, because each outcome's data type decides its method.
Unequal spreads handled explicitly
With standard deviations of 1.1 and 1.6 days, the report uses the unequal-variance t test rather than pooling two spreads that plainly differ.
A null result read correctly
Missing the 0.05 threshold is reported as insufficient evidence of a difference, with the interval showing the challenger could be anywhere from slightly slower to a day faster.
Variability as a business finding
The challenger's wider spread matters for inventory planning even without a mean difference, and the report tells logistics why predictability may outweigh a faster average.
Where marks go in BUS-352 Topic 6
This topic can be marked wrong outright, and the first place it happens is the choice of test. Treating the on-time outcome as a measurement and running a t test on it, where the course expects a comparison of two proportions, is typically marked as the wrong procedure. Treating the two carriers' shipments as paired when they are different shipments, or pooling variances that clearly differ, produces a statistic the design does not support. An assumption asserted rather than examined leaves the result resting on faith. The most common interpretive loss is reading a p-value above 0.05 as proof the carriers perform the same, when the interval shows a difference of up to a day is still plausible. Recommendations that switch carriers on a half-day gap the test could not confirm overstate the evidence in the other direction.
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BUS-352 Topic 6 questions, answered
When should the two samples be treated as paired?
When each observation in one group is matched to a specific observation in the other, such as the same routes shipped by both carriers or the same stores measured before and after a change. Pairing removes differences between routes or stores from the comparison, which usually sharpens it. When the groups are simply different shipments, as in this example, an independent-samples procedure is the correct one.
Why use the unequal-variance t test?
Because the pooled version assumes both carriers have the same underlying spread, and these samples suggest they do not. The unequal-variance version, often called Welch's test, drops that assumption and adjusts the degrees of freedom, at very little cost when the spreads happen to be similar. Many analysts use it by default for two independent means, and the example states that choice rather than leaving it to the software.
If the result is not significant, should the company keep the current carrier?
Not automatically. A non-significant result says the data do not clearly show a difference in average lead time, and the interval shows the challenger might be up to a day faster. The decision can reasonably turn on other evidence: price, the on-time comparison, and the challenger's less predictable deliveries. The example recommends a longer trial with routes assigned at random, which would answer the question more cleanly.