Answers and explanations · Deliver an analysis another person can inspect

Lesson 12 answers

12A. A good plan names the 240-shipment descriptive window, shipment grain, strict late boundary, independent simulated trial, checklist−control duration contrast and a 2-minute worthwhile threshold. It labels one additional shipment question exploratory. Preserve the initial plan, changes, reasons, dates and their effect on analysis scope. No single exact exploratory question is required.

12B. For “21.25% late”: source=data/shipments_clean.csv; unit=shipment; late=actual_minutes>promised_minutes; accepted timing records=240; late numerator=51; 51/240×100=21.25%; no additional exclusion. The raw audit reconciles to that clean file. A peer should reproduce both counts before rounding the percentage.

12C. The arithmetic mean of the 40 observed task values can remain a correct descriptive mean of tasks. An independent-person standard error, t-test and confidence interval would no longer have their stated design support because observations cluster within four people. Request the assignment unit and repeated-measure structure and use an appropriate design/method rather than relabelling rows as people.

Checkpoint marking guide

Award credit for sound reasoning in the learner's own words. Do not require verbatim wording. Each question's components sum to its stated points.

Checkpoint A answers 20 points

A1. One row per order versus one per joined item (1); denominator 80 evaluable orders (1); pre-aggregate/deduplicate by order while preserving outcome definitions (1).

A2. Mean=30/5=6 (1), median=4 (1), high 16 pulls mean upward while median depends on rank (1).

A3. Possible data-entry error (1), genuine long/difficult job or different process (1), investigate and document while retaining plausible unverified values in the primary result (1).

A4. Response fraction alone does not establish similarity between respondents and nonrespondents (2). Compare known characteristics or response rates by late status/service/other relevant measured categories (1), while acknowledging unobserved opinions remain unknown (1).

A5. Random sampling selects units from a target population (1) and supports population coverage/generalization under the design (1). Random assignment allocates intervention among enrolled units (1) and supports causal contrast under experimental assumptions (1).

A6. Histogram or appropriate individual-value plot (1); x=duration minutes, y=count for equal-bin histogram (1); a valid concern such as bin width, hidden tail, unequal-bin area or misleading scale (1).

Checkpoint B answers 20 points

B1. Total flags=135 (1), positive predictive fraction=40/135≈29.63% (1), 40/50=80% is the catch rate among delayed jobs (1).

B2. SE=8/√64=1 (1); SD describes individual variation (1); SE estimates repeated-sample variability of the mean under independence/model assumptions (1).

B3. Margin=2×2.1=4.2 (1); interval [25.8,34.2] (1); concerns the model mean, not individual durations (1); 95% describes procedure coverage under assumptions (1).

B4. Under the null model and assumptions, a statistic at least as extreme in either direction has probability 0.03 (2). Not probability the null is true (1), nor probability the effect is practically important or that a specific causal explanation is correct (1).

B5. Interval includes meaningful differences in both directions (1), non-rejection is not proof of equality (1), equivalence needs a pre-specified acceptable margin and suitable procedure (1).

B6. Selective reporting/multiple searching raises false-positive risk (1), disclose all analyses and label exploration (1), pre-specify a primary outcome or use suitable multiplicity control/independent confirmation (1).

Checkpoint C answers 20 points

C1. −2 percentage points (1); relative −20% (1); baseline explicitly 10% (1).

C2. Overall rates are weighted subgroup averages (1); group weights can differ by hub (1); an easy-route-heavy hub can rank higher overall despite lower rates within strata (1).

C3. One more km corresponds to 1.8 higher fitted minutes within the descriptive model (2). Does not show that shortening a route causally saves exactly 1.8 minutes (1).

C4. Any two, explained: curvature, heteroskedasticity/changing spread, dependence, omitted variables, influential points, poor out-of-sample performance (up to 3 for two sound explanations).

C5. Interval excludes zero (1), crosses −2 so worthwhile reduction not established (1), simulated evidence cannot prove real benefit (1), propose appropriately designed real pilot/measurement rather than guaranteed rollout (1).

C6. Names checklist−control and negative=faster (1), gives estimate and 95% Welch interval for mean difference (1), n=20 per group/40 total (1), labels simulated data and avoids individual/real-world guarantees (1).