Certified Quality Engineer Training

Explore the basic elements of the quality system through Juran's trilogy—quality planning, quality control, and quality improvement—and define system, management system, and quality management system.

Explore the quality system documentation: quality policy, quality manual, procedures, work instructions, and forms, and note ISO 9001-2015's shift to documented information replacing documents and records.

Understand the right level of documented information under ISO 9001-2015, covering documents and records, distribution, access, storage, retention, and disposition, plus basic revision control and configuration management.

Explore ISO 9001 standards, ISO 9000 overview, and the Baldridge award, with emphasis on the three-layer certification system: certification body, accreditation body, and IAF.

Discover the ISO 9000 series basics, ISO 9001:2015 certification requirements and transition from 2008, ISO 9004 guidelines for sustained success, and ISO 19011 auditing guidance revised in 2018.

Explore ISO 9001 revision history from 1987 to the 2015 version, noting the switch from twenty clauses to eight to ten main clauses and major upgrades in 2000 and 2008.

Explore why ISO 9001:2015 revised from 2008, focusing on reduced documentation and the creation of a common structure for multiple management systems through Annex SL, aligning standards.

Explore ISO 9001:2015 changes from 2008, including updated terms (product and services, documented information, external providers), no exclusions, process approach, and risk-based thinking.

Map ISO 9001:2015 clauses four to ten to the PDCA cycle, covering context, leadership, planning, support, operation, performance evaluation, and improvement.

Explore quality audits by classifying into product, process, and system audits, including internal, external, and first party, second party, third party types, with planning, implementation, reporting, and follow up.

Explore first party, second party, and third party audits, including internal vs external classifications and terms like registration and compliance audits for ISO 9001.

Identify the three audit participants—the client, auditor, and auditee—and outline each party's roles and responsibilities, including lead auditor duties, opening and closing meetings, and corrective actions.

Expands audit roles by introducing technical experts, observers, and guides, detailing how they support the audit team with domain knowledge, non-interference observation, and facility navigation.

Master the audit cycle from planning and preparation through opening and closing meetings, interviews, reporting, and follow-up, using objective evidence to address non-conformities.

Learn how to craft an accurate, traceable, objective, and clear audit report, perform timely follow-up with corrective and preventive actions, verify results with documentary evidence, and ensure proper audit closure.

Explore Taguchi loss function across three cases: nominal is best, smaller is better, and larger is better, with examples like exhaust, dead pixels, and yield.

The Addie model guides quality training with five stages—analyze, design, develop, implement, and evaluate—covering needs analysis, learning objectives, content design, and Kirkpatrick evaluation.

Apply the Kirkpatrick model to assess training effectiveness across four levels—reaction, learning, behavior, and results—showing how training reduces defects using seven quality tools, including histograms and control charts.

Explore Garvin's quality dimensions and how they guide product and service design, using House of Quality to translate customer needs into design decisions.

Explore ten service quality dimensions, including reliability, responsiveness, and tangibles. Understand how they consolidate into five: reliability, assurance, tangibility, empathy, and responsiveness.

Assess the 2022 CQE updates: section 3 questions drop from 23 to 21, while section 3A remains unchanged except for adding the word 'assess'.

Identify how design inputs shape product and service design per ISO 9001:2015 clause 8.3. Focus on input and control (review, verification, validation); outputs and changes are not covered in CQE.

Explore design inputs and controls, including customer needs, regulatory requirements, codes and standards, past designs, and failure analysis, using Taguchi robust design, FMEA, QFD, DFX, and design for six sigma.

Improve product quality by designing for robustness, minimizing variation from noise factors while keeping control factors in check for consistent outputs.

Explore robust design by identifying control factor settings that minimize the impact of noise factors on outputs, focusing on parameter design and values for voltage, current, and electrode type.

Explore robust design by identifying outer noise, inner noise, and between product noise to minimize their impact. Learn how temperature changes, shock, vibration, humidity, and deterioration affect the process.

Minimize the effect of noise on the output through robust design by reducing noise, considering interactions, and addressing non-linear effects. Demonstrating steadier drop weight yields more consistent joint strength.

Apply failure modes and effects analysis (FMEA) to identify and prioritize design and process risks, conducting conceptual, design, or process FMEA at system, subsystem, or component levels.

Master the FMEA process: identify process steps and failure modes, calculate severity, occurrence, and detection to derive RPN, prioritize high risks, and update the living document with actions.

Use quality function deployment, or the house of quality, to translate customer needs into design inputs by ranking importance and linking what customers want to how you deliver it.

Explore design for x, focusing product design on the voice of the customer via quality function deployment and goals like manufacturability, cost, assembly, and logistics, balancing trade-offs.

Design for manufacturing and assembly by reducing parts and simplifying features, then apply maintainability, reliability, logistics, and environment considerations to optimize product life cycle.

Explore the 2022 asq cqe 3b updates, adding ctq and reorganizing 3b1, 3b2, and 3b3 to cover design inputs, techniques, and review, plus converting voice of customer to ctqs.

Learn to interpret drawings and specifications for the CQE exam, including dimensions, tolerances, and geometric dimensioning and tolerancing, plus first and third angle projections.

Learn how to construct and interpret the first angle projection symbol by drawing four quadrants, projecting top, side, and front views, and unfolding them onto the back wall.

Illustrates first angle projection for an l-shaped piece, showing front, right-side, and top views, unfold to reveal connected projections and dotted lines clarifying the layout.

Demonstrate third angle projection by placing the object in the third quadrant and applying walls between viewer and object, yielding front, top, and side views and the symbol.

Explain the title block essentials of a manufacturing drawing, including drawing number, sheet number, revisions, approvals, units, scale, tolerances, bill of materials (bom), notes, and zones.

Learn to use eight common line types in technical drawings, including construction, boundary, hidden, centerline, dimension, break, cutting, and hatch lines, to convey boundaries, visibility, and cross-sections clearly.

Explore dimensioning in 3c drawing: chain, parallel, and running dimensioning. See how chain tolerances accumulate and how parallel and running dimensioning save space from a single origin.

Explore tolerances on drawings to ensure interchangeable parts, with upper and lower limits, plus minus tolerances, and learn how lmc and mmc affect hole, pin fits: clearance, transition, and interference.

Understand geometric dimensioning and tolerancing (GD&T) and how it governs pin and hole fits, including taper and perpendicularity, with rules for making and checking shared by supplier and buyer.

This lecture explains geometric dimensioning and tolerancing (GD&T) using a glass sheet example, detailing flatness within 0.2 mm and datum plane perpendicularity to reference plane A, via symbols.

Clarify datum versus datum feature in 3C GD&T. A datum is a perfect reference, while a datum feature is the tangible surface used to locate the part.

Define datum features using the gd&t position symbol and three perpendicular datum planes A, B, and C to lock six degrees of freedom and enable precise hole drilling.

ASQ CQE 2022 updates show that sections 3C and 3D remain unchanged in the body of knowledge, with only minor subtext revisions in 3C and no topics added or removed.

Explain ISO 9001 design controls: design review, design verification, and design validation. Show verification as input-output checks and validation as testing the product in service for its intended use.

Define reliability as the probability a device performs its required function under stated conditions for a given time, and explain how design, manufacturing, and wear-out cause failures with load–strength variation.

Learn how to measure reliability using MTTF, MTBF, MTTR, and availability. Distinguish repairable from non-repairable failures and understand failure concepts.

Explore how mean time to failure (MTTF) measures reliability for non repairable items by analyzing a lifecycle, failure timing, and the impact of sample size on average bulb life.

Explore mean time between failures (MTBF) as the reliability measure for repairable items, calculated as total operating hours divided by failed units, with the reciprocal giving the failure rate.

Distinguish mean time to failure for non repairable items from mean time between failures for repairable items, using two examples with 100 items tested over 10,000 hours.

Explore mean time between failures (MTBF) and how exponential distribution governs reliability, including a 2-year MTBF example yielding about 36% no-failure probability and related failure-rate concepts.

Compute the hazard rate as the instantaneous failure rate from yearly data, illustrated by 1000 units with year-by-year failures, showing a bathtub curve with burn-in, constant, and wear-out phases.

Explore the bathtub curve, detailing non-repairable and repairable items with burn-in, constant hazard, and wear-out phases, and introduce probability, exponential, and Weibull methods to compute reliability.

Explore the bathtub curve and hazard function via the Weibull distribution, with shape parameter kappa and scale lambda, covering initial, constant, and wear-out phases to assess reliability.

Explore basic probability concepts and how reliability relates to probability, using dice, a 1000-bulbs example, and Venn diagrams to analyze union and intersection events for system performance.

Explore mutually exclusive events, independent events, dependent events, and complementary events with dice and Venn diagrams, clarifying intersections and complements.

Learn the probability multiplication rule for two events, distinguishing independent and dependent cases, and compute the chance of A and B both occurring using dice and drawing from a bowl.

Explore reliability by calculating the probability a missile hits its target, illustrating how two independent missiles raise success from 0.8 to 0.96 using the union rule.

Use a tree diagram to compute the probability of at least one hit with two independent missiles, each hitting with 0.8 probability, yielding 0.96.

Explore how series and parallel configurations affect system reliability; compute reliability by multiplying component reliabilities for series and noting that parallel uses an or condition.

Learn to calculate mixed series and parallel system reliability, solving parallel subsystems first, then multiplying series reliabilities, with notes on binomial, Poisson, Weibull, and exponential distributions for the CQE exam.

Explore the Weibull distribution as a continuous reliability model, with shape and scale parameters, highlighting its nonnegative support, hazard function, and how k values shape burn-in, constant, and wear-out phases.

Relate the exponential distribution to the constant hazard region of the Weibull model by setting k=1, yielding probabilities e^{-t/mtbf} or e^{-lambda t}.

Learn how exponential distribution, a special case of Weibull with k=1, uses P(t)=e^{-t/MTBF} to estimate survival; for MTBF=200 h, P(200)=0.3677 (36–37%), not 50%.

Determine the mean time between failure for an exponential distribution using a reliability of 0.904 at 1000 hours and natural log to solve for MTBF.

Use the exponential distribution with a constant failure rate to estimate survival at 1500 hours, given a mean time between failure of 500 hours.

Master fault tree analysis (fta) as a reliability tool, using and gates and or gates. Compute series reliability as R1*R2 and parallel reliability as 1-(1-R1)(1-R2), applying to subsystems.

Explore fault tree analysis by calculating reliability for series and parallel components using and/or gates, illustrated with a multi-level example reaching near-perfect system reliability.

This lecture covers the 2022 ASQ CQE updates to the body of knowledge for reliability and maintainability, including use FMEA and hazard analysis, and explains dFMEA, pFMEA, and uFMEA.

Explore methods to control production and service delivery, covering control plan development, critical control point identification, work instruction development, and validation, plus acceptance sampling and measurement system analysis.

Use a control plan to monitor and control product and process characteristics, involve the process owner, revise as lessons are learned, and apply xbar r charts.

Identify critical control points within HACCP and learn how CCP identification acts as a stop sign to ensure safety, control, and adherence to the seven HACCP principles.

Develop work instructions to document step-by-step tasks, ensuring consistency and the ability to improve processes over time, then validate outcomes to confirm the product meets its intended use.

Understand material identification, status, and traceability, including PMI, mill test reports (MTRs), and lot numbers, to ensure materials are used and traceable under ISO 9001 guidance.

Identify and implement material segregation by keeping good and bad components separate, quarantined material aside, and color-coding carbon steel and stainless steel to prevent mix-ups.

Differentiate defects from nonconformities and learn ISO 9001 requirements for nonconforming outputs. Apply containment, correction, corrective actions, and update the risk register and quality management system to prevent recurrence.

Explore the material review board (MRB) process for nonconforming products, detailing MRB members, roles, and decisions to accept under concession, scrap, rework, regrade, or return to supplier.

Learn the basics of acceptance sampling, deciding to accept or reject a lot from a sample, using an 80-piece sample with a 1.5% AQL and an acceptance number of 3.

Learn how acceptance sampling uses samples with AQL and acceptable quality limits to decide lot acceptance, and understand producer's and consumer's risks, including type 1 and type 2 errors.

Explore acceptance sampling standards for attribute and variable sampling, compare ANSI/ASQ z1.4 and z1.9 with MIL-STD 105 and 414, and learn go/no-go decisions, acceptable quality limit concepts, and OC curve.

Explore acceptable quality limit, its role in acceptance sampling, and OC curves showing the probability of accepting a lot with defects, including producer's risk at 1.5 percent.

Explore how acceptable and rejectable quality limits shape sampling plans, define producer and buyer risks, and use alpha, beta, LTPD, and the OC curve to assess lot acceptance.

Explore the operating characteristic curve, its link to AQL and RQL, and how alpha, beta, and the three zones—acceptable, rejectable, and indifferent—guide acceptance sampling.

Define acceptance thresholds with AQL and RQL, and explain how alpha and beta affect OC curves. Use a Poisson sampling plan with 80 items and acceptance up to 3 defectives.

Use the Poisson distribution to calculate the probability of accepting a lot by sampling 80 items with up to 3 defectives, and plot the oc curve across different defect levels.

Illustrate OC curve for a sampling plan using Poisson distribution, linking AQL, RQL, alpha, and beta to acceptance probabilities. Show how increasing sample size steepens the curve and reduces RQL.

Explain how aoq and aoql relate to a sampling plan of 80 samples with an acceptance number of 3, and how ati varies with input defectives.

Explore attribute sampling standards like MIL-STD-105 and Z1.4, based on AQL or the average quality limit, and compare with the Dodge-Romig plan using LTPD, RQL, and AOQL.

Choose the inspection level, set the AQL to 1.5% per MIL STD 105 / Z1.4, and select a single sampling plan with normal inspection: sample 80, accept 3, reject 4.

Explore attribute acceptance sampling with two examples (lot sizes 1000 and 50), using general inspection level 2 and AQLs of 1.5, 1.0, and 2.5, via code letters and arrows.

Explore MIL 105 inspection levels I, II, III and S1–S4, how sample size and discrimination shape the OC curve, with codes H, F, and J and reduced, normal, tightened inspection.

Understand the three inspection types—reduced, normal, and tightened—and the terminate option. Learn how moving among them depends on prior lot acceptance, defectives, and sampling rules, including double sampling nuances.

Compare single, double, and multiple sampling in quality control, detailing AC/RE values, and sample sizes (n, n1, n2) with cumulative rejections.

Compare the dodge romig sampling plans with MIL STD Z1.4, focusing on LQL and AOQL tables, single or double sampling, and process average to minimize ATI and protect the customer.

Study variable sampling in quality control, using MIL STD 414 to determine sample size, compute mean and standard deviation, and decide acceptance via a quality index.

Ensure sample integrity by drawing a random, unbiased sample that truly represents the lot, then label and preserve it for potential destructive testing to support accurate acceptance decisions.

Explore measurement tools in cqe, including tape, vernier calipers, micrometers, gauge blocks, and optical comparators, focusing on least counts, reading methods, rule of 10, and destructive versus nondestructive tests.

Compare destructive and nondestructive tests, detailing tensile, impact (Charpy) and fatigue tests, stress–strain concepts, ductility and necking, plus nondestructive methods like radiography, ultrasonic, magnetic particle, liquid penetrant, and hardness testing.

Learn nondestructive tests for material and weld evaluation, including radiography, ultrasonic, magnetic particle, liquid penetrant, and hardness tests. Radiography uses X-rays or gamma rays to reveal internal voids and defects.

Survey nondestructive testing methods such as radiography, ultrasonic testing, magnetical particle testing (MPI), and liquid penetrant testing, and discuss hardness testing with portable rebound devices.

Explore how the coordinate measuring machine (CMM) was added in 2022 and how it measures complex geometry using multiple X, Y, Z points with touch or non-touch probes.

Examine measurement system analysis by reviewing operator, instrument, and part variation, define the reference value as a proxy for the true value, and apply the ten-to-one rule to gauge resolution.

Understand how accuracy differs from precision and how bias, linearity, and stability shape measurement results. See how calibration, operating range, and drift influence bias over time.

Explain gage repeatability and reproducibility (gage R&R) as variations from the gauge and operator, and introduce the precision-to-tolerance ratio to assess whether a system meets tolerance, with PTR 10 percent.

Apply the range method to quickly assess gage R&R by having two operators measure five parts, compute the average range, and compare percent GRR to process sigma to judge capability.

Learn the average and range method for gage R&R, splitting GRR into repeatability and reproducibility, and verify results with SigmaXL and manual calculations.

Learn the difference between crossed and nested gage R&R studies: crossed uses multiple operators measuring each part, suitable for nondestructive tests, while nested assigns one measurement per part for destructive tests.

Explore how a histogram, a bar chart, visualizes the frequency and spread of measurements, using water bottle and arrival-time examples to reveal center, variation, and causes of deviation.

Apply a Pareto chart to prioritize quality improvements by the 80/20 rule, sorting issues from largest to smallest and targeting the vital few until 80 percent of problems are addressed.

Explore how a scatterplot shows the relationship between two variables, with the independent variable on the x-axis and the dependent variable on the y-axis, using temperature and ice cream sales.

Stratification divides data into categories to reveal category-specific quality issues, using separate pareto charts for 300 ml, 500 ml, and 1000 ml bottles to uncover distinct problems.

Organize large sets of ideas from brainstorming using the affinity diagram (K-J method) to group post-it notes into natural categories and reveal actionable themes.

Explore how a tree diagram breaks a goal, like passing six sigma black belt exam, into motivation, books, videos, and quizzes, and compare with fault tree and cause and effect.

Apply a prioritization matrix to rank products using weighted criteria, multiply each criterion’s importance by its rating on a 1–5 scale, and sum for the final launch decision.

Explore kaizen, the step-by-step improvement method, and kaizen blitz, a rapid, focused waste-removal approach. Document current state, identify wastes, implement changes, standardize, and celebrate gains.

Explore the PDCA cycle—plan, do, check, act—as an iterative, Deming‑inspired approach to planning, implementing, evaluating, and refining process improvements.

Identify the current constraint, exploit existing resources, subordinate all activities to the constraint, elevate the constraint with additional action, and anticipate new constraints as they move.

Learn how 5s drives workplace organisation by sorting, setting in order, shining, standardising, and sustaining to reduce waste and boost space utilisation, productivity, morale, and safety.

Learn how visual controls in lean manufacturing convey shop floor information at a glance, using Kanban, Andon, and Judoka systems, plus 5S organization to prevent defects.

Learn how lean and Six Sigma reduce waste by identifying Muda, Mura, and Muri, including Type 1 and Type 2 Muda, to balance flow and boost profits.

Identify the seven types of muda plus the eighth, underutilisation, using Timwood; explore transportation, inventory, motion, waiting, over-production, over-processing, and defect.

Identify motion waste within a process, reduce waiting and overproduction, prevent overprocessing and defects, and apply poka-yoke to leverage worker knowledge and reduce inventory waste.

Standardized work, built on 5s approach, turns routine tasks into a consistent method, enabling waste reduction and quality through steps, time, tools, and procedures, and provides a baseline for improvement.

Analyze process flow metrics such as work in progress, work in queue, touch time, takt time, cycle time, throughput, and lead time with simple examples.

Learn how SMED reduces setup time and inventory by quickly changing dies. Explore external and internal setup, standardization, clamps, intermediate jigs, and parallel operations to boost machine utilization.

Detail the 2022 CQE updates, shifting from muda to eight types of waste and adding overall equipment effectiveness (OEE), defined by availability, performance, and quality.

Identify the root cause of an undesirable event and eliminate it to permanently resolve problems, using RCA to distinguish root causes from symptoms and guide corrective actions.

Explore the 2022 updates to ASQ CQE section 5E, which adds 5 Whys as a corrective-action tool, with examples, best practices, and how to address root causes and policy issues.

Explore poka-yoke, invented by Shigeo Shingo in the 1960s, as a preventive action tool from Toyota's system, using prevention and detection devices with USB and gear examples, plus robust design.

Apply robust design as preventive action to minimize variation. In welding, use controllable factors like electrode, position, and heating to reduce outer noise, inner noise, and between product noise.

Explore qualitative and quantitative data, and differentiate continuous versus discrete data with real-world examples and measurement tools like go/no-go gauges.

Explore the four data scales—nominal, ordinal, interval, and ratio (NOIR)—with emphasis on order, difference, absolute zero, and central tendency options (mode, median, mean) across qualitative and quantitative data.

Develop a data collection plan by defining goals, setting an operational definition, selecting data types and sampling, and using reliable time-based measurements like assembly time with a stopwatch.

Learn how to apply data coding to simplify recording, using addition, subtraction, multiplication, or truncation, and understand how these methods affect the mean and standard deviation.

Analyze data accuracy and integrity, identifying bias, knowledge gaps, boredom, rounding, and falsification. Implement safeguards like automation and audits, and preview descriptive statistics, inferential statistics, and hypothesis testing.

Explore descriptive statistics by summarizing data through central tendency and variability, using mean, median, mode, percentile, quartile, range, and standard deviation (sigma) for six sigma.

Explore central tendency by comparing mean, median, and mode, and learn how extreme values bias the mean while percentile and quartile partition data.

Explore measures of dispersion including range, inter quartile range (Q3-Q1), and standard deviation. The video explains sample and population forms, the role of n-1, and illustrates calculations with data examples.

Master stem-and-leaf plots, box-and-whisker plots, and scatterplots to visualize data relationships; learn how stems and leaves organize data and interpret summaries from mpg examples.

Learn how to build box-and-whisker plots by identifying Q1, Q2 (median), Q3, and the interquartile range, then draw the box and whiskers and flag outliers.

Explore graphical methods for depicting distributions, compare box and whisker plots, histograms, and QQ plots, and assess normality using p values and 95 percent confidence.

Unpack the CQE 6A upgrades with data automation, data integration, and data visualization dashboards, highlighting automated data collection, a unified data view, and real-time KPI dashboards.

Explain type 1 and type 2 errors with a perfume bottle example. Discuss alpha and beta, producer's and consumer's risk, and how sample size affects power.

Explain six steps of hypothesis testing, from formulating Ha and H0 to selecting alpha, calculating the test statistic, and interpreting one- or two-tailed results for a mean like 150 cc.

Learn to read the z table to find z critical values for single- and two-tail tests at alpha levels like 0.05, 0.01, and 0.10, covering 90%, 95%, and 99% confidence.

Understand how p-values guide hypothesis testing, showing that low p-values reject the null while high p-values fail to reject it, within a 95 percent confidence framework.

determine the required sample size from the desired margin of error and confidence interval using z-values, sigma, and appropriate formulas for continuous data or proportions.

Define probability via the classic model as outcomes where the event occurs over total possible outcomes, relating to experiments, events, and sample spaces, including relative frequency, with a six-sided die.

Explore how Venn diagrams illustrate union and intersection of events using dice examples, and define mutually exclusive, independent, and complementary events.

Explore the addition and multiplication rules in probability, including union and intersection, mutually exclusive events, and independent versus dependent cases with dice, marbles, and coin examples.

Explore factorials, permutations, and combinations, define 0! = 1, and distinguish between when order matters or not, with formulas for nPr and nCr and practical examples.

Summarize 2022 updates to ASQ CQE body of knowledge 6B to 6H, noting no major changes, except 6C removing bivariate normal and chi-square topics, plus minor cognitive-level and subtext tweaks.

Explore the normal probability distribution, its symmetry, mean, median, and mode equality, and how to use the standard normal (z) score to compute probabilities with z-tables or software.

Explore the Bernoulli distribution as a single-trial variant of the binomial and contrast it with the hypergeometric distribution for sampling without replacement from a finite population.

Explore Poisson distribution for discrete data, compare it with binomial, and compute probabilities using mu and the formula e^{-mu} mu^x / x!, noting the mean equals variance for rare events.

Explore how random samples produce a point estimate via the sample mean to estimate the population mean, and how interval estimates bound the true parameter.

Understand how sample size, standard deviation, and confidence level shape the width of a confidence interval, and how larger samples, lower sigma, and higher confidence widen the range.

Compute a 95% confidence interval using the z table when the population standard deviation is known or the sample size is large, illustrated with a 100-sample salary example.

Learn to calculate a confidence interval with the t distribution when the population standard deviation is unknown and the sample is small, using degrees of freedom n minus one.

Calculate the confidence interval for proportions with p ± z alpha/2 sqrt(p(1-p)/n), using np>5 and n(1-p)>5 checks, illustrated by defectives in a 100-item sample.

Explore one-sample and two-sample hypothesis tests for mean, variance, and proportions, including z, t, paired t, p tests, chi-square, and ANOVA, to assess population changes.

Use a z test to compute z from X bar, meu, sigma, n. With X bar 152, meu 150, sigma 2, n 100, apply two-tailed 95% confidence and reject Ho.

Explore the one-sample t test, its t statistic formula, and how to decide whether a sample mean differs from a hypothesized mean using a 95% two-tailed test, with an example.

Explore two-sample z test and the null hypothesis mu1 equals mu2. Learn to compute z from x1 bar and x2 bar with sigma1^2/n1 plus sigma2^2/n2, using a perfume machine example.

Explore the two-sample t test, including independent and dependent data and paired t test, learn how to compute t using pooled variance and compare to t critical, with examples.

Apply a two-sample t test with equal variances to compare machine A and B, compute means, pooled standard deviation, t value, and 95% confidence to decide on volumes.

Explore two-sample t tests for equal and unequal variances, apply the correct formula, and interpret p-values and degrees of freedom to decide whether to reject the null.

Compute the paired t test using the differences between before and after scores, then compare the t value to the critical value with n-1 degrees of freedom to assess significance.

Explore tests for variance and standard deviation, including the f test for equality of two variances, the chi-square test for population variance, and anova-related applications.

Compare a sample variance to a population variance using a one-sample chi-square test with n-1 degrees of freedom. Interpret null and alternative hypotheses and the p-value at 95 percent confidence.

Understand the analysis of variance (anova) for testing equality of several means with the F test. See why anova controls error rates when comparing more than two means.

Explore how ANOVA compares means across three machines, using variation between and within groups to test if at least one machine differs, via box plots.

Explore ANOVA and variance concepts, focusing on sum of squares, between and within components, and how degrees of freedom yield mean sum of square and the f value.

Perform manual ANOVA calculations by computing the sum of squares between and within, degrees of freedom, and the F value, then verify results with a Microsoft Excel ANOVA demonstration.

Perform a single-factor ANOVA in Excel to compare three machines, interpreting the F value, p value, and F critical to reject the null hypothesis.

Learn how to apply chi-square goodness-of-fit tests to determine if a sample follows a specified distribution, using observed versus expected frequencies, with null and alternative hypotheses and degrees of freedom.

Master contingency tables by calculating expected values, such as non smokers male, and determining degrees of freedom using (r-1)(c-1) in a two-by-two setup.

Learn how correlation measures how two variables fluctuate together, using hours studied as the independent variable and exam marks as the dependent variable, plus scatter diagrams and the correlation coefficient.

Compute the Pearson correlation coefficient quickly in Excel with the data analysis pack. Observe how you select input ranges and output results to a new worksheet.

Explore the correlation coefficient r as a measure of the strength and direction of the linear relationship between x and y, shown by hours studied and marks obtained.

Learn how to estimate population correlation from a sample by using Fisher's transformation to z, calculate a 95 percent confidence interval with variance 1/(n-3), and convert back to r.

Explore how the coefficient of determination (r squared) measures the share of variance in the dependent variable explained by the independent variable, illustrated by hours studied and marks.

Derive the regression equation y = a + b x to predict marks from hours studied, using the best-fit line with intercept and slope that minimizes squared distances.

The lecture explains time series and run charts, showing how time on the x-axis reveals trend and seasonality in data such as stock prices and electricity use, with moving averages.

Statistical process control uses monitoring and control charts to detect deviations and trigger quick action, reducing defects and ensuring conforming products while distinguishing common and special causes.

Differentiate common causes from special causes in processes, labeling common as random or noise and special as assignable signals, and use control charts to detect and address the special causes.

Understand rational subgrouping for X-bar and R charts by taking time-specific samples of five items, calculating mean and range, and distinguishing within-subgroup and between-subgroup variation to set control limits.

Learn how to select control charts for variable and attribute data using a flowchart, choosing I-MR, X-MR, X-bar R, X-bar s, np, p, c, or u charts.

Learn to compute x bar and r charts, using constant subgroup sizes, with x bar mean, r bar range, and control limits via a2, d3, and d4.

Learn about the X bar-s chart for variable data with larger subgroups and how s bar, B-3 s bar, and B-4 s bar define control limits.

Explore attribute control charts for counting defects. Use np and p charts for defectives, and c and u charts for defects, with binomial distribution and subgroup size considerations.

Contrast p charts with np charts to show how changing subgroup size affects p bar and the upper and lower control limits, illustrated by a hospital unplanned-return example.

Apply c charts for constant subgroup size and u charts for variable size, using Poisson distribution, and compute c bar with limits c bar ± 3 sqrt c bar.

Learn how to construct a u chart for defects per unit when subgroup sizes vary, compute u bar, and set variable upper and lower control limits for each subgroup.

Interpret control charts to identify assignable causes by points beyond the upper or lower control limits (three sigma), and apply Nelson rules to recognize eight patterns signaling process changes.

Calculate Nelson rule probabilities from a normal distribution: 68% within 1 sigma, 95% within 2 sigma, 99.73% within 3 sigma, and rare patterns like seven consecutive points on one side.

This lecture explains pre-control charts that rely on specification limits for initial setup, using green and yellow zones to judge samples relative to pre-control limits and specification limits.

Learn short run SPC by focusing on the process, using a difference chart with I-MR charts and nominal sizes (300, 400, 500 mm) to set control limits.

Convert dimensions to z values and plot a z-mr chart for short-run spc, using a center line and 3 sigma limits to compute mean and sigma from moving ranges.

Explains process performance matrices such as percent defective, ppm, DPMO, and defects per unit, clarifies defect opportunities vs defects, and explains rolled through yield for process chains.

Explain how Cp and Cpk quantify process capability by comparing customer specifications to process spread, and why Cpk accounts for mean shift using a shaft diameter example.

Explain Cp from the distance between lower and upper specification limits and six-sigma spread, and Cr as the inverse of Cp, indicating the percentage of specifications used by process variation.

Assess process capability to determine if new, repaired, or adjusted equipment will meet specifications by examining mean, standard deviation, and tolerance; ensure representative, normal, statistically controlled data from 20–30 samples.

Explore Pp and Ppk as long-term performance indices that tolerate drift, compare them to Cp/Cpk, and introduce Cpm with Taguchi’s target concept and centering.

Define the response as the outcome (dependent variable) in design of experiments, with factors as controlled variables. Distinguish numeric and categorical factors with examples like car mileage and course sales.

Explore how sugar and milk levels interact in experiments using interaction charts and box plots; learn to identify noninteraction when two lines run parallel.

Explore contour plots as a way to visualize responses by connecting equal values, interpret straight versus curved lines for interaction, and compare ratings like 6, 7, and 8.

Create a predictive equation for y using milk and sugar inputs, with intercept b0 as the average of values, and show how sugar and milk effects 1.5 each predict outcomes.

Define the objective, select factors and the response, design and conduct experiments (full, partial, or Plackett-Burman), and analyze results with anova to guide screening, optimization, and robustness decisions.

Explore how sugar and milk interact to affect coffee ratings using design of experiment, larger samples, and three visuals: interaction charts, contour plots, and an interaction-inclusive equation.

Study a two-factor factorial design with interaction for sugar and milk. Derive the design of experiments equation by calculating B0, Bs, Bm, and the Xs.Xm interaction to predict ratings.

Define factors and levels as controlled variables, explain treatments and responses, and analyze effects and interactions, while using randomisation and replication to ensure unbiased results in design of experiments.

Explore design and analysis of experiments with three factors at two levels, using sugar, milk, and bean, coded minus/plus, with results shown on a rating cube.

Explore half factorial design, reducing eight full-factor experiments to four using diamond or circle corners in a three-factor, two-level coffee study and c = a·b, plus resolution and blocking.

Identify nuisance factors and apply blocking, randomisation, and ANCOVA to manage their impact. Understand balanced and unbalanced designs.

Explore completely randomized one-factor experiments that use random assignment to balance unknown nuisance factors and reduce bias, illustrated by a treatment A vs placebo study in 30 patients.

Block randomized design controls known nuisance factors, such as sex, by blocking first (16 male, 14 female) and then randomizing within each block to treatment and placebo, ensuring balanced groups.

Learn how two-level factorial experiments scale from full factorial to half factorial, analyzing confounding, and understanding resolution 3, 4, and 5 and how main effects alias with interactions.

Define risk as the effect of uncertainties on objectives, per ISO 31000 and ISO 9000:2015. Explain risk management through identification, assessment, prioritization, and resources to minimize harm or maximize opportunities.

Identify risks systematically through group brainstorming and analytical tools (Ishikawa diagram, flow diagrams, SWOT, FMEA), compile a risk register, and prioritize risks for action using RPN.

Prioritize risks with a probability and impact matrix to target high priority items, and compare qualitative analysis with FMEA’s risk priority number, considering cost, schedule, scope, and quality.

Apply qualitative risk analysis with a probability and impact matrix to identify high-priority risks and plan responses, avoid, mitigate, transfer, or accept negatives and exploit, enhance, share, or accept positives.

Handle negative risks using four strategies: avoid by changing plans, mitigate by simplifying processes and developing a prototype, transfer via insurance or subcontracting, and accept with active contingency planning.

Apply these four options to positive risk: exploit, enhance, share, and accept, and monitor these actions as part of risk management. Put your best team members and resources to work.

Discover the 2022 changes to section 7 of the CQE BoK, adding risk based thinking and new risk management topics, including planning, types, evaluation and auditing, monitoring, and mitigation.

Learn how risk based thinking emerged from ISO 9001:2015, identify risk in planning, and apply proportionate actions while evaluating effectiveness to continuously update risk.

Explore enterprise, operational, and product risk management, including risk-based thinking, swot analysis, and internal controls to anticipate and mitigate threats.

Identify four components of risk management planning: objective, risk criteria, stakeholder identification, and team members' roles and responsibilities. Define risk ownership and the risk manager's accountability ahead of risk evaluation.

Explore risk management evaluation by comparing auditing and testing of controls, focusing on conformance and non-conformities, the risk register, and the effectiveness of internal controls to detect and prevent risks.

Demonstrate mitigation planning as part of risk management by reducing the probability or impact, simplifying processes, prototyping for validation, and using inspections and lessons learned to reach full production.