Before any season-to-season comparison, the MT241 Unit 5 data quality review audits a pro hockey club's two-season file field by field and rules on which differences are real. Searches like "mt 241 unit 5 assignment example", "mt241 unit 5 sample" and "mt241 unit 5 example" land here.
What a finished MT241 Unit 5 data quality review looks like
Five pages centered on a field table with six rows: shot attempts, shot locations, expected goals, hits, giveaways and in-house zone entries. Each row records who produces the value, how it is recorded, whether that changed between the 2024-25 and 2025-26 seasons, and a verdict. Zone entries carry the largest change: the club's own video staff kept pass-ins in a separate category in the first season and counted them as controlled in the second. Hits show a building effect, 26.4 per game recorded at home against 19.1 on the road, with the same team on the ice. The vendor's expected-goals values for 2024-25 came from an older model version and were never recomputed. Three 2025-26 games are missing from the video file after a camera failure.
How a MT241 Unit 5 example is structured
Fields, not seasons, organize the review, because each field has its own recording history and a single verdict on the whole file would be wrong for most of it. The first column names the source of every value: league scorers in the arena, a data vendor's model, or the club's own video staff. Recording method comes second and change between seasons third, which is where the review earns its keep. Each change is then tested rather than asserted. Hits are compared home against road for the same team; zone entries are recoded under the new rule for a ten-game sample from the first season, which shows how much of the eleven-point rise the definition alone explains. Missing games are listed with their dates. A closing table states which season-to-season comparisons the file can support, which need adjustment, and which it cannot support at all.
Six fields, three sources
Arena scorers record shot attempts, locations, hits and giveaways; a vendor supplies expected goals; the club's video staff codes zone entries. Each source is named first, because each fails in its own way.
Pass-ins, recoded
Ten games from 2024-25 were recoded under the second season's rule. Controlled entries in that sample rose from 41 to 49 percent on definition alone, leaving about three points of the apparent rise for any real change.
Hits depend on the building
The same club was credited with 26.4 hits a game at home and 19.1 away. Analysts have documented arena-to-arena differences in how scorers log hits and giveaways, and the review rules both unfit for comparison across buildings.
Two model versions
The vendor retrained its expected-goals model between seasons and left the earlier values as they were. The review asks for recomputed history and, until it arrives, permits expected-goals comparisons within a season only.
Games missing from the video file
A camera failure left three 2025-26 games uncoded. Zone-entry rates for that season rest on 79 games, and the review lists the missing dates so later units do not treat the file as complete.
Where marks go in MT241 Unit 5
Declaring a dataset clean or messy in general, without testing a single field, is how a Unit 5 review falls flat in MT241, because the drawer's question is whether two seasons were counted alike and only a field-by-field check can answer it. Changes asserted without evidence draw deductions nearly as often: a review claiming a definition changed should show, as the recoded sample does, how far that change moves the figure. Building effects are a known feature of hockey scoring, and a review silent on them misses the standard example. Missing records left unmentioned let later analysis overstate its sample. Some drafts identify problems and stop, never ruling on what the file can still be used for, which leaves the coaching staff's question open. Vendor data deserves the same scrutiny as the club's own, though drafts often exempt it.
Get a MT241 Unit 5 example written to your instructions
Two seasons, two leagues or two providers: whichever pair the Unit 5 review sets against each other, attach the file or its field list with the rubric. Within 24-48h, and free the first time, the review comes back field by field, each change tested where the data allows and a verdict on which comparisons still stand.
MT241 Unit 5 questions, answered
What is scorer or arena bias in hockey data?
Differences in how official scorers in different buildings record the same kinds of events. Hits, giveaways, takeaways and even shot locations have shown arena-to-arena variation that the teams playing there cannot explain. Analysts often adjust for it or avoid comparing such counts across buildings, and a data quality review should check for it before trusting a home-road difference.
How can a review test whether a definition change moved a figure?
By recoding a sample of the earlier data under the new definition and measuring the difference. If recoding raises the rate in the sample by about as much as it rose between seasons, the rise is mostly definitional. The example recodes ten games, enough to estimate the effect without redoing a full season of video.
Is vendor-supplied data reliable enough for course assignments?
Usually, for the purpose it was built for, but not automatically across years or providers. Vendors update models, change collection methods and sometimes revise past values. A review should record which version produced each figure and avoid comparing numbers from different versions. Asking the vendor for recomputed history, or limiting comparisons to one season, are both defensible responses.