ASQ Certified Six Sigma Green Belt (CSSGB): key terms

Flashcards for this exam ASQ Certified Six Sigma Green Belt (CSSGB) guide

• 5SLean workplace organization method: Sort, Set in order, Shine, Standardize, Sustain.Foundational
• Affinity DiagramTechnique for organizing a large number of ideas into groups based on natural relationships.Foundational
• Alternative Hypothesis (H1)The claim that there is an effect or difference, accepted when the null hypothesis is rejected.Foundational
• Analysis of Variance (ANOVA)A test of whether the means of three or more groups differ, by comparing between-group variance with within-group variance (the F-statistic).Intermediate
• AndonLean visual signal (cord, button, or light) used to alert that a problem has occurred and work should stop to address it.Intermediate
• Bayes' TheoremA rule for updating the probability of a hypothesis given evidence: P(A|B) = P(B|A) × P(A) / P(B).Intermediate
• Binomial DistributionThe distribution of the number of successes in n independent trials, each with the same success probability p.Intermediate
• Box PlotA chart showing a distribution's median, quartiles (the box), whiskers and outliers; also called a box-and-whisker plot.Foundational
• Central Limit Theorem (CLT)The theorem that the distribution of sample means approaches a normal distribution as sample size grows, regardless of the population's shape.Intermediate
• Check SheetA structured form for collecting data in real time at the location where it is generated.Foundational
• Chi-Square Test (χ²)A test for categorical data: goodness of fit (observed vs expected frequencies) or independence (association between two categorical variables).Intermediate
• Cluster SamplingDividing the population into clusters (such as schools or stores), randomly selecting whole clusters, and sampling everyone (or a sample) within them.Intermediate
• Coefficient of Variation (CV)The standard deviation divided by the mean, often given as a percentage, used to compare variability across datasets with different scales.Intermediate
• Common Cause VariationNatural, inherent variation in a stable process due to many small random factors.Intermediate
• Conditional ProbabilityThe probability of an event given that another event has occurred: P(A|B) = P(A and B) / P(B).Intermediate
• Confidence Interval (CI)A range computed from sample data that, under repeated sampling, would contain the true population parameter a stated percentage of the time.Intermediate
• Control ChartA graph showing process data over time against a mean and upper and lower control limits to determine whether a process is stable.Foundational
• Control PlanDocument describing the actions, measurements, and responses required to sustain process improvements.Intermediate
• Control QualityProcess of monitoring and recording quality activity results to ensure outputs are complete, correct, and meet expectations.Intermediate
• Correlation CoefficientPearson's r: a value from -1 to +1 measuring the strength and direction of a linear relationship between two numeric variables.Intermediate
• Cost of Poor Quality (COPQ)Costs that would disappear if every activity were performed without defects.Intermediate
• Cost of Quality (COQ)All costs of preventing nonconformance, appraising conformance, and failing to meet requirements over the product life.Foundational
• Critical to Quality (CTQ)A measurable characteristic of a product or process that is critical to customer satisfaction.Intermediate
• Defects per Million Opportunities (DPMO)Six Sigma metric: (defects / (units x opportunities per unit)) x 1,000,000.Intermediate
• Descriptive StatisticsStatistics that summarize and describe the features of a dataset, such as its center, spread and shape, without generalizing beyond it.Foundational
• Design of Experiments (DOE)Statistical method for determining which factors and interactions affect a process output.Advanced
• DMADVSix Sigma design methodology: Define, Measure, Analyze, Design, Verify.Intermediate
• DMAICSix Sigma improvement cycle: Define, Measure, Analyze, Improve, Control.Foundational
• Empirical RuleThe 68-95-99.7 rule: in a normal distribution about 68%, 95% and 99.7% of values lie within 1, 2 and 3 standard deviations of the mean.Foundational
• Expected Value (EV)The probability-weighted average of all possible outcomes of a random variable: E[X] = Σ x × P(x).Intermediate
• Failure Mode and Effects Analysis (FMEA)Structured method for identifying potential failure modes, their causes and effects, and prioritizing actions.Intermediate
• First Pass Yield (FPY)Percentage of units completing a process step correctly the first time without rework.Intermediate
• Fishbone DiagramA cause-and-effect diagram that organizes potential causes of a problem into categories.Foundational
• Fitness for UseQuality definition attributed to Joseph Juran: a product is of quality if it meets real needs of its users.Intermediate
• Five WhysRoot cause technique of asking 'why' repeatedly, typically about five times, to move from symptom to underlying cause.Foundational
• GembaJapanese for 'the real place' where value is created; a gemba walk is going there to observe work.Intermediate
• Grade vs. QualityQuality is how well characteristics meet requirements; grade is a category for items with the same use but different technical characteristics.Foundational
• HeijunkaLean technique of leveling production by volume and mix over time.Advanced
• HistogramA bar chart showing the frequency distribution of data.Foundational
• Hypothesis TestingA statistical procedure for deciding whether sample evidence is strong enough to reject a null hypothesis about a population.Intermediate
• Inferential StatisticsUsing sample data to draw conclusions or make predictions about a larger population, with quantified uncertainty.Foundational
• Interquartile Range (IQR)The spread of the middle 50% of data: the third quartile minus the first quartile (Q3 - Q1).Foundational
• JidokaLean principle of automation with a human touch: stopping the process when a defect is detected.Intermediate
• Just-in-Time (JIT)Producing what is needed, when it is needed, in the amount needed.Foundational
• KaizenJapanese term for continuous, incremental improvement involving everyone.Foundational
• KurtosisA measure of the tailedness of a distribution relative to a normal distribution.Advanced
• Lean ThinkingManagement philosophy derived from the Toyota Production System focused on maximizing customer value while minimizing waste.Foundational
• MeanThe arithmetic average: the sum of values divided by the number of values.Foundational
• Measurement System Analysis (MSA)Evaluation of a measurement system's accuracy, precision, and stability before relying on its data.Advanced
• MedianThe middle value of ordered data, or the average of the two middle values when the count is even.Foundational
• MudaJapanese term for waste: any activity that consumes resources without adding customer value.Foundational
• Muri and MuraLean terms for overburden (muri) and unevenness or variability (mura), which together with muda are the 3 Ms.Intermediate
• Normal DistributionA symmetric, bell-shaped continuous probability distribution fully described by its mean and standard deviation.Foundational
• Null Hypothesis (H0)The default assumption of no effect, no difference or no relationship, which a test tries to find evidence against.Foundational
• One-Tailed TestA hypothesis test in which the alternative specifies a direction (greater than or less than), so the rejection region lies in one tail.Intermediate
• OutlierAn observation that lies unusually far from the other values in a dataset.Foundational
• P-ValueThe probability, assuming the null hypothesis is true, of observing results at least as extreme as those in the sample.Intermediate
• Paired t-TestA t-test on the differences between paired measurements of the same subjects, such as before and after a treatment.Advanced
• Pareto ChartA bar chart sorted in descending order of frequency with a cumulative percentage line, used to identify the vital few causes.Foundational
• Poisson DistributionThe distribution of the count of events in a fixed interval of time or space, when events occur independently at a constant average rate λ.Intermediate
• Poka-YokeLean technique of mistake-proofing a process so errors are prevented or immediately detected.Intermediate
• Population and SampleThe population is the entire group of interest; a sample is the subset actually observed and measured.Foundational
• Precision vs. AccuracyPrecision is consistency of repeated measurements; accuracy is closeness of a measurement to the true value.Intermediate
• Process CapabilityThe ability of a stable process to produce output within specification limits, measured by indices such as Cp and Cpk.Advanced
• Pull SystemA system in which work is pulled into a stage only when capacity is available, rather than pushed.Foundational
• Quality AuditA structured, independent review of whether project activities comply with organizational and project policies and procedures.Intermediate
• Regression AnalysisStatistical technique modeling the relationship between a dependent variable and one or more independent variables.Advanced
• Risk Priority Number (RPN)FMEA score calculated as severity x occurrence x detection.Intermediate
• Rolled Throughput Yield (RTY)Probability that a unit passes through all process steps defect-free: the product of each step's first-pass yield.Advanced
• Rule of SevenControl chart heuristic: seven consecutive data points on one side of the mean indicate a non-random pattern.Intermediate
• Scatter DiagramA graph plotting pairs of numerical data to show the relationship between two variables.Foundational
• Seven Basic Quality ToolsClassic quality tools: cause-and-effect diagram, flowchart, check sheet, Pareto chart, histogram, control chart, and scatter diagram.Foundational
• Sigma LevelA measure of process capability expressing how many standard deviations fit between the mean and nearest specification limit.Advanced
• Significance Level (α)The threshold probability α for rejecting the null hypothesis, commonly 0.05, which equals the accepted Type I error rate.Foundational
• Simple Random Sampling (SRS)A sampling method in which every member of the population has an equal chance of being selected.Foundational
• Single-Minute Exchange of Die (SMED)Lean method for reducing setup or changeover time to under ten minutes.Advanced
• SIPOCHigh-level process map listing Suppliers, Inputs, Process, Outputs, and Customers.Foundational
• Six SigmaData-driven quality methodology aiming to reduce process variation to no more than 3.4 defects per million opportunities.Foundational
• Six Sigma BeltsRole structure in Six Sigma: Champion, Master Black Belt, Black Belt, Green Belt, Yellow Belt, and White Belt.Foundational
• SkewnessA measure of a distribution's asymmetry: right (positive) skew has a long right tail, left (negative) skew a long left tail.Intermediate
• Special Cause VariationVariation arising from specific, assignable causes outside the normal process.Intermediate
• Specification LimitsCustomer- or engineering-defined boundaries (USL and LSL) for acceptable output.Intermediate
• Standard Deviation (SD)The square root of variance, describing typical distance from the mean in the data's own units.Foundational
• Standard Error (SE)The standard deviation of a sampling distribution, usually of the mean: SE = s / √n.Intermediate
• Statistical PowerThe probability that a test correctly rejects a false null hypothesis (1 - β); 80% is a common target.Advanced
• Statistical Process Control (SPC)Use of statistical methods, chiefly control charts, to monitor and control a process.Advanced
• Student's t-TestA test comparing means (one sample against a value, or two independent groups) when the population standard deviation is unknown, using the t-distribution.Intermediate
• Systematic SamplingSelecting every kth item from an ordered list after a random start.Intermediate
• Takt TimeThe rate at which products must be completed to meet customer demand: available production time divided by customer demand.Intermediate
• Theory of Constraints (TOC)Management philosophy by Eliyahu Goldratt holding that every system is limited by at least one constraint.Advanced
• Two-Tailed TestA hypothesis test in which the alternative is non-directional (≠), splitting α across both tails of the distribution.Intermediate
• Type I ErrorRejecting a true null hypothesis: a false positive, with probability α.Intermediate
• Type II ErrorFailing to reject a false null hypothesis: a false negative, with probability β.Intermediate
• VarianceThe average squared deviation from the mean, measuring how spread out data are; units are the square of the data's units.Intermediate
• Visual ManagementLean practice of making work, standards, and abnormalities visible at a glance.Foundational
• Voice of the Customer (VOC)Process of capturing customer needs, expectations, and preferences.Foundational
• Western Electric RulesSet of decision rules for detecting non-random patterns on control charts.Advanced
• Z-ScoreThe number of standard deviations a value lies from the mean: z = (x - μ) / σ.Intermediate
• Z-TestA hypothesis test using the standard normal distribution, appropriate when the population SD is known or samples are large, including tests of proportions.Intermediate