LSS Black Belt · PeopleCert · Analyze · Bank updated 2026-07-03
LSS Black Belt practice questions: Analyze
5 free questions from 27 on this area · answer and explanation for each · no sign-up
These 5 questions come from the Analyze section of our LSS Black Belt bank (27 questions on this area, which carries 20% of the real exam). Every question is original, with the correct answer explained and linked to the source it is drawn from.
1/5 · Analyze · easy
A Black Belt wants to estimate average call-handling time across a call center that has four distinct call types with very different average durations. To ensure every call type is proportionally represented in the sample rather than relying on chance, which sampling method should be used?
ASimple random sampling from the entire population of calls
BConvenience sampling of whichever calls are easiest to record
CSystematic sampling by selecting every 10th call regardless of type
DStratified random sampling, sampling proportionally within each call type
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2/5 · Analyze · hard
A two-way ANOVA examines the effects of Machine (2 levels) and Shift (3 levels) on output yield. The Machine x Shift interaction term has a p-value of 0.02 at alpha = 0.05, while both main effects (Machine and Shift individually) are non-significant. What is the correct interpretation?
AThe interaction p-value is invalid because at least one main effect must be significant for an interaction to be tested
BThe design should be discarded and replaced with a one-way ANOVA
CThe effect of Machine on yield depends on which Shift it operates in, so the main effects should not be interpreted in isolation
DBecause both main effects are non-significant, the significant interaction result should be ignored
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3/5 · Analyze
A quality analyst samples a process whose individual measurements have a population standard deviation of 12. Applying the Central Limit Theorem, if the analyst increases the sample size used to compute each sample mean from n = 9 to n = 36, what happens to the standard error of the sample mean?
AIt stays at 4, because once a sample of at least 30 is reached the Central Limit Theorem states the standard error becomes independent of sample size.
BIt increases from 4 to 8, because averaging more individual observations increases the variability of the resulting estimate.
CIt decreases from 4 to 1, because the standard error is inversely proportional to the sample size itself rather than its square root.
DIt decreases from 4 to 2 (cut in half), because the standard error equals the population standard deviation divided by the square root of the sample size.
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4/5 · Analyze · easy
A Black Belt sets alpha = 0.05 for a hypothesis test comparing a new supplier's part dimension to the historical mean. The Black Belt rejects the null hypothesis and concludes the mean has shifted, but in reality the supplier's true mean has NOT changed. Which risk has just been realized?
ASampling error that is unrelated to the concepts of hypothesis testing risk.
BType II error (beta risk), because a real difference was not detected.
CA violation of the Central Limit Theorem that invalidated the test.
DType I error (alpha risk), because a true null hypothesis was incorrectly rejected.
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D is correct. A Type I error occurs when a true null hypothesis is incorrectly rejected, concluding a difference exists when none does; its probability is exactly the alpha level set for the test. A Type II error is the opposite case: failing to detect a real difference. Recognizing which error occurred in a given scenario is a core Analyze-phase skill.
↗ the IASSC Lean Six Sigma Black Belt Body of Knowledge — Analyze
5/5 · Analyze
A simple linear regression model relating machine temperature to product weight yields an R-squared of 0.91. Which statement correctly describes what this R-squared value tells the Black Belt?
AThe correlation coefficient r between temperature and weight must be negative, since 0.91 is close to 1.
BMachine temperature causes 91% of the variation observed in product weight.
C91% of the individual future predictions made by the model will fall within the process's specified tolerance.
DApproximately 91% of the variability in product weight can be statistically explained by its linear relationship with machine temperature.
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D is correct. R-squared (the coefficient of determination) quantifies the proportion of variability in the response variable that is statistically explained by the fitted regression model. It does not indicate causation, does not describe prediction accuracy against a tolerance, and does not reveal the direction of the relationship, which is instead conveyed by the sign of the correlation coefficient r.
↗ the IASSC Lean Six Sigma Black Belt Body of Knowledge — Analyze
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