Six defects in 21,684 order lines, each counted and traced to its effect on fill rate or on-time delivery, make up the composite MT438 audit written for Unit 2. Searches like "mt 438 unit 2 assignment example", "mt438 unit 2 sample" and "mt438 unit 2 example" land here.
What a finished MT438 Unit 2 data quality audit looks like
A defect register takes up most of six pages, one row per problem, with columns for the test used, lines affected, likely cause and the metric it distorts. Order resends through electronic data interchange left 388 exact duplicates, 1.8 percent of lines. Another 1,146 lines, 5.3 percent, record the ordered quantity in pallets while shipments are recorded in bags, at fifty bags a pallet. Ship timestamps are missing on 1,927 lines, 83 percent of them from the Macon plant after a scanner change in mid-March. Sixty-four lines ship before they were ordered. Seventeen products exist under two item numbers since a packaging redesign. Requested delivery dates on 6,830 lines equal the order date plus seven days, the system's default. A second table recomputes both headline metrics.
How a MT438 Unit 2 example is structured
The extract is described before it is judged: source system, date range, row count and the fields examined. Tests come next, each written so another analyst could repeat it, such as matching on order, line, item and quantity to find duplicates, or flagging quantities under ten on items sold only by the bag. Findings follow in order of their effect on the metrics rather than their count. The unit-of-measure and duplicate problems lead because together they move fill rate by more than five points, and they offset each other, so fixing only one would make the figure look worse or impossible. Missing timestamps come next, handled by bounds rather than deletion: on-time delivery lies between 84.2 and 93.3 percent. The remaining three defects close the register with a cleaning rule for each and a note on who owns the fix.
Tests anyone could repeat
Every defect is found by a stated rule. Duplicates match on order number, line, item and quantity; pallet quantities appear as values under ten on bag-only items.
Two errors hiding each other
Correcting pallets alone takes fill rate to 92.6 percent. Removing duplicates alone pushes it to 101.3, a figure no shipment record can produce, which is how the pallet problem gives itself away.
Blanks from one plant
Macon's missing timestamps began with a scanner change on March 16. Because Macon's recorded lines run late more often, dropping the blanks would flatter on-time performance.
A range instead of a guess
Counting every blank as late gives 84.2 percent on time; counting every blank as on time gives 93.3. If blanks behave like Macon's recorded lines, the estimate is about 91.8.
Seventeen products, two histories
Items renumbered after the bag redesign split their sales in two. Merging them matters before any forecast, since each half looks like a product in decline.
The plus-seven default
Nearly a third of requested dates are the system's default. The audit recommends measuring on-time delivery against promised dates until customers' real requests are captured.
Where marks go in MT438 Unit 2
Data quality audits in MT438 are marked on whether each defect is tied to its consequence. A list of problems without their effect on a metric reads as housekeeping; showing that two errors together hide more than five points of fill rate reads as analysis. Tests should be stated precisely enough to rerun. Instructors often penalize deleting incomplete records without comment, because the missing rows are rarely random, and here they come from the plant with the weaker record. Bounds or a stated assumption handle missing values more honestly than silence. Papers that clean the data and then report only the corrected figures miss the chance to show how wrong the original was. Defaults posing as data, like the plus-seven date, are the defect graders most often see overlooked.
Get a MT438 Unit 2 example written to your instructions
Forward the order extract or data description attached to your Unit 2 materials, the assignment brief and the rubric. The composite audit written from it states each test, counts affected rows, traces every defect to the metric it distorts, and handles missing values with bounds. Your opening custom sample is free of charge, within 24-48h.
MT438 Unit 2 questions, answered
Should I delete bad records or fix them?
It depends on the defect. Exact duplicates can usually be removed. Unit errors can often be corrected with a conversion factor. Missing values are trickier, because deleting them assumes they resemble the rest, which is rarely true. Whatever you choose, state the rule, count the rows affected and show the metric before and after, so the grader can judge the choice.
How do I find unit-of-measure errors?
Look for quantities that are implausible for how an item is sold, such as a 3 on a product always ordered in hundreds, or ratios between ordered and shipped quantities that cluster at a pack size like 12 or 50. Checking the item master for its selling unit helps. Once found, a single conversion rule usually corrects most of them.
What if my extract has no obvious errors?
Look harder at fields that could be defaults: dates that sit a fixed number of days after another date, codes that repeat far too often, or quantities that are round numbers in suspicious volume. Also check whether totals reconcile to a known figure. A clean extract is possible, but saying which tests you ran and what they found is still the substance of the audit.