Certified Quality Technician (2026)

Explore quality concepts and tools. Learn about customers and suppliers, quality principles, standards, requirements, specifications, and cost of quality; cover the seven basic quality tools and problem solving methods.

Explore external and internal customers, intermediate and end users, and impacted parties, plus the voice of the customer to align quality with purchase orders and customer-specific requirements.

Explore basic quality terminology and the model of features—basic, performance, and excitement—then examine fitness for use, defects, and six M's of variability in processes.

Explore continuous improvement in quality, from incremental gains to breakthrough automation, and distinguish monitoring from measuring while grasping specifications and quality standards like ISO 9001, AS9100, IATF 16949, ISO 17025.

Discover the two major halves of quality in manufacturing: quality planning before parts are made and quality control after parts are made, to ensure parts conform to requirements.

Learn how the production part approval process (ppap) confirms supplier understanding of engineering requirements and process capability through a pap package including PSW, GR&R, performance tests, and FMEA.

Connect the cost of quality to business value through four categories—prevention, appraisal, internal failure, and external failure—and guide cost reduction across buckets.

Use an actionable, practical Excel template to build a cost of quality reporting system, covering prevention, appraisal, internal, and external costs, with monthly and annual KPI insights.

Explore the seven basic quality tools, including check sheets, Ishikawa diagrams, flow charts, histograms, Pareto charts, scatter plots, and control charts, to analyze data and visualize quality problems.

Explains how flowcharts and swim lane diagrams map processes and responsibilities, and demonstrates histograms as data visualization with bins, frequency, and the mode to drive quality decisions.

Explore Pareto charts to prioritize defects by frequency, highlight the significant few, use the cumulative line, and examine scatterplots that relate independent inputs to dependent outcomes.

Visualize data with scatter plots and use linear regression to predict nail height from air pressure, explore correlations, and introduce run charts and control charts, including variable and attribute charts.

Explore how correlation measures the association between two variables, identifying positive and negative relationships and strength, using Pearson's correlation coefficient and its covariance and standard deviation components.

Explore how to quantify the relationship between process temperature, material density, and flexural strength through a scatterplot and correlation case study, using Excel analysis and illustrative three point flexural test.

Analyze a case study dataset in excel to compute the correlation between input temperature and flexure strength, visualize with scatter plots, and derive a regression line and r-squared.

Explore five why analysis as a brainstorming tool to move past symptoms toward root causes, and understand how failure analysis and nondestructive testing support quality management and continuous improvement.

Apply the five why by three layers technique to analyze defects across the occurrence, escapement, and systemic layers, identify root causes, and guide corrective actions.

Utilize the 5 why x 3 layers A5Y worksheet to diagnose root causes with a cross-functional team, using process, detection, and systemic layers, and download the Excel template.

Explore the 8D problem solving method, a Ford-originated universal framework to identify, contain, and implement permanent corrective actions to prevent recurrence across processes.

Explore six sigma as a measure of dispersion, visualize the normal or bell curve, and apply sigma ranges to describe population dispersion and process range.

Apply six sigma as a data guided method to reduce process variation and defects, aligning process output with the voice of the customer within specification limits through the dmaic cycle.

Explore quality function deployment and the house of quality to translate the voice of the customer into measurable design features and performance metrics.

Learn design of experiments as a statistical method to plan, conduct, and analyze how inputs affect outcomes. Explore factors, levels, interactions, and designs like full and fractional factorials.

Learn lean manufacturing as a waste reduction approach that delivers customer value by eliminating muda and seven wastes—transportation, inventory, motion, waiting, over processing, overproduction, and defects.

Explore the 5S method—sort, set in order, shine, standardize, sustain—as a lean manufacturing system, incorporating value stream mapping and pull system to boost morale and efficiency in areas and labs.

Apply a practical 5s audit template in excel to implement sort, straighten, shine, standardize, and sustain at your workplace, with customizable questions, simple scoring, and conditional formatting.

Use value stream mapping to identify value-added versus non-value-added steps in a manufacturing process, map from raw materials to finished goods, and measure cycle and wait times to reduce waste.

Explore pull vs push inventory systems, where a customer order or forecasted demand triggers production, contrast with push methods, and explain the push–pull boundary shaping how supply chains combine approaches.

Explore the dmaic continuous improvement cycle: define, measure, analyze, improve, control — and apply common lean six sigma tools like flow charts, Pareto analysis, run charts, and scatter plots.

Explore the Deming cycle (plan, do, check, act) with prototyping and data analysis to verify improvement, leveraging brainstorming, benchmarking, APQP, and process capability analysis for cross-functional teams and value engineering.

Outlines the American Society for Quality code of ethics, detailing expectations to act with integrity and honesty, demonstrate responsibility and fairness, and safeguard proprietary information while avoiding conflicts of interest.

Explore ten downloadable blog posts as PDFs that extend foundational quality concepts, including Six Sigma and the Deming cycle, and provide next steps for ongoing growth beyond the course.

Explore statistical techniques for quality professionals, including describing populations, testing differences, sampling plans, frequency distributions, central tendency, dispersion, confidence limits, probability, control charts, process capability, and common and special causes.

Examine population and sample and how sample statistics infer population parameters through inferential statistics. Explore statistical process control with tool families: capability analysis, Pareto analysis, control charting, and regression analysis.

Explore how frequency distributions distinguish discrete and continuous data, visualize patterns with histograms, and apply mean and standard deviation formulas to understand uniform and other distributions.

Explore measures of central tendency within the normal distribution, focusing on the mean (mu) and the sample mean x-bar, and compare median and mode, including bimodal data and histograms.

Explore measures of dispersion, focusing on standard deviation as the key descriptor of variability in normal distributions, with range, sigma, and 68-95-99.7 rule, plus notes on population and sample stdev.

Learn about confidence levels and limits, the sample mean as a point estimate of the population mean, and calculating confidence intervals with the z distribution for two-tailed tests.

Formulate null and alternate hypotheses and determine one- or two-tailed tests, then apply the z test (or t test) statistic to compare machine B to machine A at 95% confidence.

Compute z critical for a 95% one-tailed test and compare to z statistic; with z=8.43 exceeding z critical 1.645, reject null and conclude machine B is faster than machine A.

Explore probability basics, including events, complementary events, empirical vs theoretical probability, and how observed frequencies approach theory through dice rolls and reliability examples.

Explore compound events, focusing on intersection and union, using Venn diagrams and independence to calculate probabilities. Apply the rules P(A∩B)=P(A)P(B) for independent events and P(A∪B)=P(A)+P(B)−P(A∩B) with deck and market examples.

Master probability with permutations and combinations, defining permutations as ordered arrangements and factorials. Apply p(n,k)=n!/(n−k)! and c(n,k)=n!/(n−k)!k! to examples like seven contestants and the lottery 40 choose 6.

Explore statistical process control, its use of control charts to minimize variation around the target, and Taguchi's continuous quality loss function for practical quality improvement.

Expose control chart terminology and key metrics like x bar, x double bar, and R. Differentiate specification limits from control limits and view how run charts evolve into control charts.

Identify safety critical characteristics, high-defect or customer-complaint related features, and those customers request; chart only 2–5 key process input and output variables that truly drive quality.

Explore common and special cause variation and apply rational subgrouping on x-bar charts to distinguish between within-subgroup and between-subgroup variation.

Explore the X-bar and R chart through a case study of pipe length cutting, with samples every 50 pieces to set control limits. Build the X-bar chart in Excel.

Build your first x-bar and r control charts in Excel from a 75-sample, 1500-piece saw-cutting study. Use x double bar, r bar, and A2 to set control limits with SPC.

Analyze x bar and r charts to confirm central values, control limits, and spec limits, ensuring an in-control, in-spec process with a narrow range and a board-ready report.

Explore x-bar and s charts in pipe-length study, showing why the s chart uses all data to provide a better dispersion estimate than r chart, with nine samples minimum.

Construct x bar and s charts in Excel using 12-sample data to monitor dispersion, compute x double bar and s bar, and apply a3, b3, b4 control limits.

Explore p chart attribute charts using discrete data to compare good versus no good (pass versus fail) across lots; build and interpret chart in Excel with formulas and control limits.

Learn to build a p chart in Excel from real production data by calculating proportion defective and upper and lower control limits to detect unusual variation.

Explore the np chart and differentiate it from the p chart, and learn to count defects in 500-piece lots, using whole-number defect counts in automated manufacturing.

Use an np chart in Excel to compute the average defects per lot and p bar, then set upper and lower control limits and flag out-of-control points.

Explore the c chart, a defect-count control chart for a single sample using the Poisson distribution, highlighting defects per item such as paint blemishes on automotive bumpers.

Explore the c chart mechanics with single-sample data, computing c bar and three sigma tolerance, establishing upper and lower control limits, and interpreting special versus normal variation in defect counts.

Explore the u chart, the last control chart, which uses the Poisson distribution to monitor defects per standardized unit when sample size varies in bulk processes like fabric.

Explains how to build a u chart in Excel by standardizing defects per square meter, computing u bar, UCL and LCL from Poisson assumptions, and interpreting out-of-control points.

Explore the individual X and moving range chart, used for 100% measurements or infrequent production runs. Understand moving range as the difference between consecutive X values and practice in Excel.

Create an individual x and moving range control chart in Excel, using absolute differences and x-bar, r-bar constants such as e2, d3, and d4 to set limits.

Interpret control charts to detect patterns and identify potential root causes, bridging data analysis with the art of quality engineering across attribute and variable charts.

Interpret control charts by examining x bar charts, control limits, and nominal specs; identify out-of-control signals like jumps and shifts, and investigate causes such as material changes or tooling.

Learn to spot trends, truncation, and lack of discrimination on control charts using Western Electric rules, determine when the process is in or out of control, and heed SPC alerts.

Explain process capability analysis as a computational method that compares specification limits to the process output, the voice of the process, and the voice of the customer.

Learn how specification limits, expressed as USL and LSL, guide process capability analysis and reflect the voice of the customer in manufacturing specs.

Explore sampling frequency options for process capability analysis, including 100% inspection, random sampling, and interval sampling, with applications from prototyping to full production and production part approval.

Explore three options for process capability analysis, from prototyping with small samples to full production, computing pp, ppk, cp, and cpk via run charts and excel.

Explore the normal distribution and its link to sample size, learn to estimate the mean and standard deviation, and apply the 30-piece rule or Student's t distribution.

Analyze the arithmetic mean and its terminology, including population versus sample and mu. Learn to compute the mean with Excel using sum, count, and average.

Compute population standard deviation using sigma = sqrt(sum (x_i - mu)^2 / N). Compute sample standard deviation using s = sqrt(sum (x_i - x_bar)^2 / (n-1)).

Leverage histogram-based process capability analysis to compare three production processes against a 25.00 ± 1 specification, using seven-bin histograms to visualize max, min, and range.

Learn how to enable Excel's data analysis tools, install the Analysis ToolPak, and access data analysis options on the data tab for the certified quality technician course.

Analyze histograms by starting with seven bins to reveal underlying probability distributions, then identify whether data follow a normal distribution, the bell-shaped and most common distribution in manufacturing and statistics.

investigate the normal distribution curve, its mean and sigma, and how 68.3%, 95.4%, and 99.7% of values fall within one, two, and three sigma, linking to ppk and cpk.

Explore how to overlay a normal probability curve on histogram data, evaluate normality, and assess process capability using CP and CPA, while noting Excel limitations and alternative tools.

Perform a capability analysis on 100 parts using digital micrometers, build a histogram, confirm normal distribution, and compute mu and sigma as groundwork for Pp and Ppk.

Compute pp and ppk, the process performance indices, from population or sample data, using the pp formula and Pu and PL with mu and sigma to derive ppk.

Explore how Pp and Ppk indices shift between population and sample scenarios, using x-bar and s to compare process spread against specification limits.

Analyze hourly sampling with a production run to build x-bar and range charts, compute x-bar, r-bar, and finalize cp and cpk calculations.

Calculate CP and CPK from production data in Excel, using x-bar and R-bar, and estimate sigma hat from r-bar and D2 to assess process capability.

Translate the math into excel by calculating x-bar, AR, d2, and sigma hat, then compute Cp, Cp upper, Cp lower, Cpk, and Ppk for sample and population.

Interpret capability analysis results by comparing process and specification ranges. See how changing upper and lower limits shifts Pp, PP, CP, CPK, and PPK and when the mean signals risk.

Explain the difference between CPK and PPK, clarifying short-term versus long-term capability, within-subgroup versus overall variation, and the dispersion measures sigma hat and s.

Explore process capability analysis with an Excel tool, covering 100% parts, batch samples, and repeated samples; learn mu, x bar, X double bar, sigma, s, sigma hat, and cp/cpk concepts.

Explore metrology and calibration, learning how to select, maintain, and verify measurement and test equipment from hand tools to coordinate measuring machines, using data analysis and calibration principles.

Explore metrology fundamentals, calibration, and the major categories of measurement equipment, from calipers and micrometers to bore and height gauges, with emphasis on accuracy, precision, and traceability.

Explore digital and dial indicators, test indicators, gauge blocks and pins, go/no-go gauges, thread gauges and pitch gauges, and optical comparators with overlays for precision measurement.

Explore digital readouts, digital scales, microscopes, and CMM configurations with probes to understand accurate, efficient quality measurement in modern labs and shops.

Explore hardness testing equipment and methods, including Rockwell, Brinell, Vickers, Knoop, and Shaw scales, emphasizing indentation-based, often destructive measurements and careful sample preparation.

Explore surface plates as flat, stable bases for dimensional measurement and profilometers that quantify surface roughness, and study universal test machines for tensile testing to measure stress and strain.

Interpret stress–strain curves, identify elastic and yield regions, and apply torque and angle measurements with wrenches, sine bars, and angle blocks; understand color measurement via spectrophotometers and the Munsell system.

Explore how color guides and light boxes reveal colors under lighting, then cover pyrometers and emissivity, laser micrometers for diameter and ovality, and machine vision for measurement and flaw detection.

Learn how to identify, control, and maintain measurement equipment using unique serial numbers and QR codes, ensuring calibration traceability per ISO 10,012 and 1725 standards.

Explore international standards, including cesium 133 based units, and distinguish working standards from reference standards, using gauge blocks and environmentally controlled laboratory verification to ensure metrological quality.

Explore customer supplied measurement and test equipment (M&TE) and how ISO 9001:2015 8.5.3 and ISO 17025 guide the handling, calibration, and safeguarding of gauges, materials, tools, and related data.

Learn how to calibrate measurement and test equipment against traceable reference standards, document results with calibration certificates, and distinguish calibration from verification in MT&E.

Understand calibration certificates, including short forms and long forms with data, showing traceability, oversight, and conformity. Learn to manage out-of-calibration gauges, assess risk, and contain suspect products.

Assess calibration error by comparing observed values to true values, identifying sensor drift, non-linearity, and hysteresis effects. Maintain accuracy through regular calibration checks and proper procedures.

Learn print reading basics for quality technicians, covering prints, blueprints, sketches, assembly drawings, and engineering drawings, along with dimensions, tolerances, nominal dimensions, and orthographic projection.

Master geometric dimensioning and tolerancing (GD&T) concepts, tolerances, nominal dimensions, and fits under ASME Y14.5, and learn how datums and feature control frames constrain manufacturing and inspection.

Explore GD&T position tolerance for hole centers defined by basic dimensions and datum A within a 0.1 mm diameter tolerance zone, and examine flatness, straightness, and surface profile.

Explore circular runout and total runout in GD&T, using a shaft with two diameters and datum A to show how a dial indicator detects 0.002 tolerance and axis runout.

Explore the three defect categories—minor, major, and critical—and how industry rules and regulatory environments shape their classification, preserving safety and functionality; see toy examples and recall implications.

Special characteristic symbols on engineering drawings flag significant characteristics that affect performance or reliability; distinguish SC from CC, with CC demanding the strictest controls to ensure safety and compliance.

Evaluate how inspection compares a product to its ideal state and tolerances defined by blueprints and ISO 9001, highlighting conformance and non-conforming lots.

Explore direct, indirect, differential, derived, and transfer measurements through hands-on examples from tape measures to bore gauges, and learn how zeroing reduces linearity error.

Master gauge selection and measurement options—from tape measures to coordinate measuring machines—while applying the 10-to-1 rule and understanding test accuracy ratio and test uncertainty ratio.

Differentiate attribute gauges from variable gauges to understand binary output and go/no go checks, and learn how pros and cons influence gauge selection in quality systems.

Explore measurement system analysis by distinguishing accuracy from precision, identifying bias, linearity, and stability errors, and assessing repeatability, reproducibility, and resolution in gauges.

Demonstrates a ten by three by three gauge r&r in Excel, using appraisers a, b, c to measure ten samples and compute x-bar and r for variation assessment.

Master rounding rules for quality technicians by applying guidelines: drop digits when the next digit is 1–4, or round up when it is 5–9, for hundredths, tenths, or whole units.

Explore significant digits and how they communicate measurement accuracy across engineering, science, and manufacturing. Apply rules for non-zero digits, zeros, and rounding to determine the correct precision in calculations.

Master unit conversion by applying conversion factors, canceling units, and calculating values across systems from pounds to grams and euros to yen, and psi to kilopascals.

Explore practical unit conversion with printable handouts and classic charts, covering metric prefixes, SI base and derived units, and English–metric equivalences for quick reference.

Trace inspection points across the manufacturing process, from supplier source inspections and receiving checks to process, first piece, final, and doc audits, organized in a cross-functional process control plan.

Identify inspection error types—technique, inadvertent, and conscious errors. Apply round robins, checklists, and measurement system analysis to reduce defects and counter fatigue, parallax, and fraud.

Explore product traceability by tracking lot and batch numbers to raw materials, plus additional data such as machine and operator IDs and time stamps enabled by automated data collection.

Compare certificates of compliance and certificates of analysis, and learn how certificates of analysis show batch-specific steel composition, test results, and regulatory compliance with formal sign-off.

Explore nondestructive testing techniques, including liquid penetrant, magnetic particle, ultrasonic, and eddy current testing, to detect surface and subsurface defects in metal components without damage.

Compare ultrasonic testing and eddy current testing: ultrasonic uses high-frequency sound to detect internal flaws and measure wall thickness, while eddy currents use electromagnetic induction to find surface defects.

Explore mechanical fatigue testing under cyclical axial, torsional, and rotational loads, covering low and high cycle fatigue, applications in aircraft, automotive, bridges, and wind turbines.

Evaluate crash testing to assess safety systems and passenger compartment protection, and summarize flammability testing, including UL 94 and electronics and furnishings applications, within the broad landscape of quality tests.

Explore sampling characteristics and sampling plans, balancing cost and uncertainty, using inferential statistics to estimate population properties, and applying acceptance-rejection criteria in lot sampling.

Set up acceptance sampling using the AQL as the percent defective, selecting lot size, sample size, and reject level per ANSI Z 1.4 2008, while considering alpha and beta risk.

Explore operating characteristic curves, average outgoing quality, and how sampling plans—single, double, and multiple—plus AQL shape lot acceptance and product quality.

Calculate defective parts per million (DPM) and defective parts per million opportunities, showing how percent values convert to ppm and why a magnifying glass view improves quality measurements.

Identify and segregate nonconforming material to prevent its shipping, determine conformance through measurement, tag and hold nonconforming lots, and involve the material review board to decide on rework or scrap.

The material review board determines disposition for non-conforming material. It selects among scrap, rework, repair, downgrade, ship as is, or customer deviation, aiming for the least total cost.

Take a pause after inspection and test to explore ten blog posts on measurement systems analysis, gauge R&R, calibration issues, Bayes theorem, and design of experiments for advancing quality engineering.

Master quality auditing by exploring audit types, components, and communication tools, and learn to compare evidence and observations against standards such as ISO 9001 and IATF 16949.

Explore basic audit types in quality systems, including system, process, and product audits, and distinguish internal, supplier, and external audits.

Define the scope and purpose against the standard, prepare and execute the audit, document and report major, minor, and observational findings, and verify closure with root causes and corrective actions.

Learn auditing tools and techniques, including checklists and questionnaires aligned to ISO 9001, with forward and backward tracing and sampling plans to collect objective evidence and determine scope.

Explore audit communication tools and interviewing best practices, using open-ended questions and follow-ups to gather objective evidence and align quality metrics with overall business objectives.

Explore risk management in quality, integrating tools like SPC, root cause analysis, and FMEA to assess and mitigate risk, and distinguish corrective from preventive action.

Apply risk assessment and management methods to identify, evaluate, and mitigate threats in manufacturing, using FMEA to prioritize defects by severity, occurrence, and detection.

Identify six key data sources—historical quality data, process information, customer feedback, standards, supplier data, and expert input—to feed risk management tools like pfmea, dfma, and trend analysis.

Learn to perform a design FMEA in Excel, evaluating severity, occurrence, and detection to compute an RPN and drive continuous improvement.

Explore process control plans as the output of continuous quality planning (pdca), detailing measurement methods, gauges, check frequency, and corrective actions to meet customer requirements.

Explore a process control plan in Excel, detailing header information, document control, and end-to-end checks from raw material to shipping.

Identify root causes through root cause analysis and implement corrective actions, including eight D, to reduce risk and cost while improving data quality and lead times in quality systems.

Explore an adaptable 8D template in Word that guides customer and supplier actions from D0 to D8, covering problem description, containment, root-cause analysis, and preventive actions.

Apply layered process auditing (LPA) to verify work against standards, engage multiple organization layers, and identify improvement opportunities as an early warning system for process variation.

Adopt a layered process auditing approach with a downloadable excel checklist, covering workstation, part, product, process, and system categories, defining roles, frequencies, corrective actions, and 8D root cause analysis.

Advance your quality management knowledge, exploring risk management, auditing, layered process auditing, with ten articles on cost of quality, first article inspection, overall equipment effectiveness, and theory of constraints.

Earn lifetime access to all course materials and future updates, plus instructor support. Apply problem solving, statistical methods, data visualizations, and auditing to reduce defects and improve customer satisfaction.

This bonus lecture provides a downloadable resource listing Udemy quality and engineering classes with previews and prices, and highlights CQT practice exams, Gage R&R simplified, lean six sigma green belt.