Run a small analysis from question to caveated finding
Task
The domain 3 capstone. Answer one question end to end -- does the practice data show different order values across regions? -- by stating the question, describing the data, checking the shape, comparing the groups, and writing a finding that states its limits.
Steps
- Write
lab/analysis/question.txt: the question, the measure (net value of valid orders), the groups (the four regions) and what decision it could inform. - Join valid orders (quantity above zero) to customers and products, and calculate each region's order count, mean and median net value. Save them to
lab/analysis/by-region.csvwith headerregion,orders,mean,median, values to two decimal places. - Record the overall mean and median in
lab/analysis/overall.txtasmean: Nandmedian: N. - Identify which region's mean is furthest above its median, and find the orders responsible. Record
skewed region: NAMEandbulk orders there: N. - Write
lab/analysis/finding.mdwith the headings## Finding,## Evidence,## Limitationsand## Next step.
Verify
These checks run in a POSIX shell with Python 3: Terminal on macOS or Linux, and on Windows Git Bash (it comes with Git for Windows) or WSL. On Windows, python3 is often a Microsoft Store placeholder that prints “Python was not found”; run alias python3=py in Git Bash first.
awk -F, 'NR>1 {print $1": "$2" orders, mean "$3", median "$4}' lab/analysis/by-region.csv
grep -E '^(mean|median|skewed region|bulk orders there): ' lab/analysis/overall.txt
grep -cE '^## (Finding|Evidence|Limitations|Next step)' lab/analysis/finding.md
East 503 orders (mean 175.56), North 515 (147.82), South 519 (147.34) and West 460 (247.23). West's mean is far above its median, and the reason is bulk orders placed there, not a region of bigger spenders: the medians are close. Overall the mean is 177.58 and the median 99.21, and the finding has all four sections. The honest conclusion is about outliers, not regions -- which is exactly what the Limitations section is for.
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