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Pilot module 2 (roadmap 8) — Process Data and Quality Decisions
Data provenance and cleaning, denominators and aggregation, variation and stability, and association versus cause — using real public garment productivity data.
What you will be able to do
Audit a real dataset before analysing it: where it came from, what each column means, its units and date range, what is missing and every change made. Choose the right denominator and aggregation so a summary number means what it claims. Read a time-ordered chart, choose subgroups that separate sources of variation, and check stability before judging capability. Recognise when a pattern in observational data is only an association, and plan a fair trial before claiming an improvement.
The practical work
Practical B1: Public-data audit with reproducible answer key. Practical B2: Factory quality analysis template.
Prerequisite: Pilot module 1 read and its quiz passed; basic spreadsheet use (sort, filter, sum, average). No statistics software needed.
Reading for this module
Read these 0 Garment Ed subjects in order, then take the quiz below.
Lessons written for this module
Guided real-garment practicals
Done with a trainer on real garments. Each has a printable worksheet with a separate trainer section.Worksheets and CSV templates are not graded online. This advanced pilot has no capstone or certificate.
- Practical B1: Public-data audit with reproducible answer key
Audit the real UCI productivity file for provenance, labels, dates and missing values, and reproduce every answer.
- Practical B2: Factory quality analysis template
Set up a blank factory record for inspection, measurement, defect, wash, lab and cost data with built-in rate formulas.
Module quiz
4 questions on Pilot module 2 (roadmap 8) — Process Data and Quality Decisions. Answer them all, then check your score before moving on. 3 correct or more is a pass. Your best score is saved on this device, and to your account when you are signed in. This is a self-check, not an accredited assessment.
1. In the UCI productivity file, all 506 empty WIP values are finishing rows. How should they be treated?
2. What does the UCI dataset provide evidence of?
3. Line A: 4% defective on 50 units. Line B: 2% defective on 1,000 units. What is the combined percent defective?
4. Rows with higher incentive show higher productivity. What is the strongest justified conclusion?
No certificate for this pilot
No certificate is issued for this advanced pilot. Only 2 of 12 modules exist, so completing them is a reading and self-check record only. Lesson reading is saved on this device, and to your account when you are signed in, as a reading record only.