The ASQ Code of Ethics is structured to guide professional conduct across various relationships. The act of failing to cite the significant contributions of other individuals in a publication directly concerns professional and academic interactions with colleagues and fellow members of the profession. The "Relations with Peers" section of the code explicitly addresses this responsibility. Specifically, Article 11 under this section mandates that members must "Give proper credit for the work of others." This principle ensures intellectual honesty and respect within the professional community, making its violation a breach of one's duties to peers.

A. Fundamental Principles: This section contains broad, overarching statements about integrity and honor, whereas "Relations with Peers" provides the specific, applicable rule.

B. Relations with the Public: This category governs interactions with the general public, such as truth in advertising and public welfare, not academic attribution among professionals.

C. Relations with Employers and Clients: This section focuses on duties like confidentiality and acting as a faithful agent, which are distinct from giving credit to professional colleagues.

1. ASQ Code of Ethics. American Society for Quality. Section IV

"Relations with Peers

" Article 11. The code explicitly states

"Members shall... Give proper credit for the work of others." This is the primary and most direct source for the answer. (Available on the official ASQ website).

2. Gryna

F. M.

Chua

R. C. H.

& DeFeo

J. A. (2007). Juran's Quality Planning and Analysis: From Product Development Through Use (5th ed.). McGraw-Hill. Chapter 2

"Companywide Assessment of Quality

" discusses the role of professional ethics

often referencing the ASQ code's principles of professional integrity

which includes proper attribution among peers.

3. Besterfield

D. H. (2018). Quality Improvement (10th ed.). Pearson. Chapter 2

"Foundations of Quality

" typically covers professional ethics and the importance of codes of conduct like ASQ's

emphasizing honesty and integrity in all professional dealings

including publications. The principle of crediting others' work is a cornerstone of such ethical conduct.

Process capability analysis is a statistical tool used to assess the ability of a process to produce output within specified limits. It yields quantitative indices like Cp and Cpk, which measure process potential and performance. A primary and highly effective use of these indices is to provide an objective, data-driven basis for comparing the performance of different processes, alternative manufacturing lines, or competing pieces of equipment. By calculating and comparing the capability indices for each alternative under consideration, engineers can select the option that is most likely to consistently produce conforming products, thereby optimizing resource allocation and minimizing future quality issues.

A. Acceptance Quality Limit (AQL) is a parameter defined for acceptance sampling plans and is determined by contractual agreement, not by a process capability analysis.

C. While the results of a capability analysis can highlight a process prone to creating defects, the analysis itself is a measurement tool; it does not inherently prevent deficiencies. Prevention requires corrective action based on the analysis.

D. The critical characteristics to measure (whether attributes or variables) are identified prior to a capability analysis, typically during design, risk analysis (e.g., FMEA), or quality planning phases.

1. Gryna

F. M.

Chua

R. C. H.

& DeFeo

J. A. (2007). Juran's Quality Planning and Analysis: From Product Development Through Use (5th ed.). McGraw-Hill. In Chapter 13

"Process Capability: Method of Analysis

" the text states

"Process capability information is useful for... choosing from among competing processes" (p. 368).

2. Montgomery

D. C. (2020). Introduction to Statistical Quality Control (9th ed.). Wiley. In Chapter 8

"Process and Measurement System Capability Analysis

" Section 8.2.4

"Some Uses of Process Capability Indices

" one of the key uses listed is in "vendor selection decisions" and "planning for sequencing of production processes

" both of which involve comparing alternatives (p. 389).

3. Breyfogle

F. W. (2015). The Certified Quality Engineer Handbook (4th ed.). ASQ Quality Press. In Chapter 23

"Measuring and Monitoring Process Performance

" a primary use for process capability data is described as: "To help in the selection of new equipment by providing a quantified metric to compare the capability of competing machines" (p. 338).

The quality policy is a formal statement from top management that expresses the organization's intentions and direction related to quality. It serves as a foundational element of the Quality Management System (QMS) and provides a framework for setting quality objectives. According to internationally recognized standards such as ISO 9001:2015, the responsibility for establishing, implementing, and maintaining the quality policy lies explicitly with the "executive level of management" or "top management." This ensures the policy is aligned with the organization's strategic direction, has the necessary authority for company-wide implementation, and that commitment and resources are appropriately allocated.

B. The manager of quality typically facilitates the QMS and advises leadership, but does not have the authority to establish a company-wide strategic policy.

C. Process improvement teams are tactical groups that work on specific projects within the framework established by the quality policy, they do not create it.

D. The quality engineering department supports the QMS through technical activities but does not set the high-level, strategic direction defined in the policy.

1. International Organization for Standardization (ISO). (2015). ISO 9001:2015 Quality management systems — Requirements.

Section 5.2.1

"Establishing the Quality Policy": This clause states

"Top management shall establish

implement and maintain a quality policy that: a) is appropriate to the purpose and context of the organization and supports its strategic direction; b) provides a framework for setting quality objectives..." This directly assigns responsibility to the highest level of management.

2. Laman

S. A.

& Silvestrini

R. T. (Eds.). (2017). The Certified Quality Engineer Handbook (4th ed.). ASQ Quality Press.

Chapter 4

"The Quality System

" p. 39: In the section on Management Responsibility

the text specifies

"Top management must establish a quality policy..." This aligns with the requirements of quality management standards and best practices.

3. Juran

J. M.

& De Feo

J. A. (2010). Juran's Quality Handbook: The Complete Guide to Performance Excellence (6th ed.). McGraw-Hill.

Chapter 13

"Strategic Deployment

" Section 13.4

"Policy Setting": This section explains that setting policies is a fundamental responsibility of senior management to provide clear direction and guide the organization's actions toward achieving its strategic goals.

4. Goetsch

D. L.

& Davis

S. B. (2016). Quality Management for Organizational Excellence: Introduction to Total Quality (8th ed.). Pearson.

Chapter 5

"Strategic Management: Planning and Execution

" p. 97: The text clarifies that the development of the organization's vision

mission

and guiding principles (which include the quality policy) is led by the Chief Executive Officer (CEO) and the executive team.

The Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology is a sequential, data-driven problem-solving framework. The 'Analyze' phase is dedicated to identifying and statistically validating the root causes of a problem. Once these causes are verified, the project transitions to the 'Improve' phase. The primary objective of the 'Improve' phase is to brainstorm, develop, test, and implement solutions that directly address and eliminate the verified root causes. Therefore, the outputs of the 'Analyze' phase (verified causes) serve as the direct inputs and foundation for the activities conducted in the 'Improve' phase.

A. Control: This phase is implemented after the solution is in place to ensure the improvements are sustained over time.

B. Define: This is the initial phase where the problem is defined and project scope is established, well before causes are known.

D. Measure: This phase focuses on collecting data to quantify the current process performance and establish a baseline.

1. Breyfogle III

F. W. (2015). The Certified Quality Engineer Handbook (4th ed.). ASQ Quality Press. In Section VI

Chapter 35

"The Improve and Control Phases

" the text states

"The improve phase focuses on the full implementation of the process changes that will eliminate the root causes identified in the analyze phase" (p. 499).

2. Purdue University Manufacturing Extension Partnership. (n.d.). The 5 Phases of Lean Six Sigma. Retrieved from Purdue University website. In the description of the DMAIC process

the 'Improve' phase is defined as the stage where "the team brainstorms solutions to address the root causes found in the Analyze phase."

3. De Mast

J.

& Lokkerbol

J. (2012). An analysis of the Six Sigma DMAIC method from the perspective of problem solving. International Journal of Production Economics

139(2)

604–614. In Section 2.1

"The DMAIC method

" the authors state

"In the Improve phase

the team develops and implements solutions for the problem

aimed at eliminating the root causes" (p. 606). https://doi.org/10.1016/j.ijpe.2012.01.009

The question requires ranking risk controls based on the established "Hierarchy of Controls," a fundamental principle in safety and quality engineering. The most effective controls are engineering controls that eliminate or isolate the hazard, followed by administrative controls that rely on human behavior.

1. Design Change (2): This is the most effective control. It is a proactive engineering control that aims to design out the hazard from the product or process, fundamentally eliminating or reducing the risk at its source.

2. Safety Devices (1): These are also engineering controls (e.g., guards, interlocks) but are typically added to an existing design to protect against a hazard. They are less effective than designing the hazard out completely.

3. Training (3): This is an administrative control. It relies on worker behavior and memory, making it the least effective of the three options as it does not physically remove or contain the hazard.

A. 1,2,3: This option incorrectly ranks safety devices as more effective than fundamental design changes.

B. 1,3,2: This option incorrectly places an administrative control (training) as more effective than an engineering control (design change).

D. 2,3,1: This option incorrectly places an administrative control (training) as more effective than an engineering control (safety devices).

1. The Certified Quality Engineer Handbook

4th Edition. ASQ Quality Press. Chapter 15

"Risk Management

" Section: "Risk Control/Mitigation

" pp. 216-217. The text states

"The most effective controls are engineering controls that are part of the design of the process or product... Less effective are administrative controls

which rely on procedures and human performance." This supports ranking design changes and safety devices (engineering) above training (administrative).

2. National Institute for Occupational Safety and Health (NIOSH). (2023). Hierarchy of Controls. U.S. Department of Health and Human Services

Centers for Disease Control and Prevention. Retrieved from cdc.gov/niosh/topics/hierarchy. This official source establishes the hierarchy as: Elimination

Substitution

Engineering Controls

Administrative Controls

and PPE. It explicitly places "redesigning equipment" (a design change) as an engineering control and "training" as an administrative control

with engineering controls being more effective.

3. Goetsch

D. L.

& Davis

S. B. (2014). Quality Management for Organizational Excellence: Introduction to Total Quality (7th ed.). Pearson. Chapter 18

"Safety

Health

and the Environment

" Section: "Hazard Analysis/Prevention

" discusses the hierarchy of controls

prioritizing engineering controls over administrative controls like training for hazard mitigation.

The symbol Ⓜ is a standard modifier used in Geometric Dimensioning and Tolerancing (GD&T) as defined by the ASME Y14.5 standard. It represents Maximum Material Condition (MMC). MMC is the state of a feature of size where it contains the most material allowed by its tolerance limits. For an external feature like a shaft, this is its largest diameter. For an internal feature like a hole, it is its smallest diameter. Applying the MMC modifier to a geometric tolerance allows for an increase in the stated tolerance (a "bonus tolerance") as the feature's actual size departs from its MMC limit, which is critical for ensuring the assembly of mating parts.

A. The symbol for minimum material condition (LMC) is a circled L (Ⓛ), which represents the state of least material.

B. "Minimum measured condition" is not a standard, defined term or symbol within the ASME Y14.5 GD&T framework.

D. "Maximum measured condition" is not a standard, defined term or symbol within the ASME Y14.5 GD&T framework.

---

1. ASME Y14.5-2018

Dimensioning and Tolerancing. The American Society of Mechanical Engineers.

Section 3.3.2

"Maximum Material Condition Symbol": "The symbol for maximum material condition is a capital letter M enclosed in a circle."

Section 2.8.1

"Maximum Material Condition (MMC)": "The condition in which a feature of size contains the maximum amount of material everywhere within the stated limits of size (e.g.

minimum hole diameter

maximum shaft diameter)."

2. ASQ Quality Press. (2017). The Certified Quality Engineer Handbook (4th ed.).

Chapter 21

Section V.B.3

"Material Condition Modifiers": This section explicitly defines the symbols for material condition. It states

"The symbol for maximum material condition (MMC) is Ⓜ."

3. Krulikowski

A. (1994). A comprehensive guide to geometric dimensioning and tolerancing. Purdue University Press.

Chapter 4

"Modifiers": "The symbol for maximum material condition is a capital M in a circle. Maximum material condition is the condition where a feature of size has the most material." (p. 29).

The correct equation for the upper control limit (UCL) of an average chart (X-bar chart) is UCL = X̄̄ + A₂R̄. In this formula, X̄̄ (X-double-bar) is the grand average of all sample means, R̄ (R-bar) is the average of the sample ranges, and A₂ is a standard control chart constant that depends on the subgroup sample size (n).

None of the provided options accurately represent this formula. The question and its options are flawed. Option (C) has the structure of an upper limit calculation (using addition) but incorrectly uses the constant D₄. The D₄ constant is used exclusively for calculating the upper control limit of a range (R) chart, whose formula is UCLᵣ = D₄R̄.

A. x-a2r: This represents the formula for the Lower Control Limit (LCL) of an X-bar chart (X̄̄ - A₂R̄), not the Upper Control Limit (UCL).

B. x-a2 r: This is identical to option A and represents the formula for the Lower Control Limit (LCL) of an X-bar chart.

C. X+ D4 R: This formula is incorrect for an X-bar chart's UCL because it uses the constant D₄, which applies to the R-chart, instead of the correct constant, A₂.

D. X-D-R: This does not represent a valid or recognized formula in statistical process control for calculating control limits.

1. Montgomery

D. C. (2013). Introduction to Statistical Quality Control (7th ed.). John Wiley & Sons.

Page 280

Section 6.2.1

Equation 6.5: The formula for the UCL of the X-bar chart is explicitly given as UCL = X̄̄ + A₂R̄.

Page 280

Section 6.2.2

Equation 6.7: The formula for the UCL of the R chart is given as UCL = D₄R̄

confirming that D₄ is the incorrect constant for an X-bar chart.

2. MIT OpenCourseWare. (2007). 15.062 Data

Models

and Decisions. Fall 2007. Lecture 21: Statistical Process Control.

Lecture Notes PDF

Slide 15: The control limits for the X-bar chart are listed as X̄̄ ± A₂R̄. The limits for the R-chart are listed as UCL = D₄R̄ and LCL = D₃R̄. This clearly distinguishes the use of the A₂ and D₄ constants.

The question describes a potential nonconformity (unlabeled product could be shipped) and asks for a preventive action. A preventive action is a proactive measure taken to eliminate the cause of a potential problem before it occurs. Forming a team to error-proof the process (also known as Poka-Yoke) is a classic preventive action. It involves redesigning the process to make it impossible for the error to occur or to make the error immediately obvious if it does. This approach addresses the system's weakness at its root, preventing future occurrences, which is the primary goal of preventive action.

A. Training is a general improvement activity and may not address the systemic root cause of the potential failure.

B. This is a correction or rework. It fixes an existing nonconforming product but does not prevent the problem from happening again.

D. A backward trace is an investigative tool used for root cause analysis, typically after a nonconformity has occurred, not a preventive action itself.

1. Gryna

F. M.

Chua

R. C. H.

& DeFeo

J. A. (2017). Juran's Quality Planning and Analysis: From Product Development Through Use (6th ed.). McGraw-Hill Education.

Chapter 10

Section 10.5

"Corrective and Preventive Action

" distinguishes between corrective action for existing problems and preventive action for potential problems. It highlights that preventive actions often involve process redesign and error-proofing to build in safeguards.

2. Breyfogle

F. W. (2015). The Certified Quality Engineer Handbook (4th ed.). ASQ Quality Press.

Chapter 10

"The Corrective and Preventive Action System

" p. 131

states

"Preventive action is a proactive process that attempts to identify and eliminate the cause of potential problems." Error-proofing is cited as a key technique for achieving this.

3. Shingo

S. (1986). Zero Quality Control: Source Inspection and the Poka-yoke System. Productivity Press.

This foundational text explains that Poka-Yoke (error-proofing) systems are designed to prevent inadvertent human errors from occurring in the first place

which is the essence of the preventive action described in option C.

4. ISO 9000:2005

Quality management systems — Fundamentals and vocabulary.

Clause 3.6.4 defines preventive action as "action to eliminate the cause of a potential nonconformity or other undesirable potential situation." This directly contrasts with option B

which addresses an existing nonconformity (a corrective action

defined in 3.6.5).

Risk treatment involves selecting and implementing options to address identified risks. The objective is to modify the risk by changing its likelihood or consequences. In this scenario, re-training employees who are creating discrepancies is a direct action aimed at reducing the likelihood of future non-conformities. By improving employee skills and knowledge, the organization is actively trying to eliminate the root cause of the errors. This action directly aligns with the risk treatment strategy of risk mitigation or reduction.

A. Generating a weekly report is a risk monitoring and communication activity. It measures and reports on risk events but does not, by itself, modify the risk.

B. Distributing a weekly alert report is also a communication activity. It raises awareness but is not a direct action to reduce the occurrence or impact of the risk.

D. Reducing throughput is a potential risk treatment strategy (e.g., slowing down to improve quality), but it was not an action described as being implemented in the scenario.

1. International Organization for Standardization (ISO). (2018). ISO 31000:2018 Risk management — Guidelines.

Section 6.5.2

"Selection of risk treatment options

" states that the purpose of risk treatment is to select and implement options for addressing risk. Options can include actions to change the likelihood of an event. Re-training directly addresses and aims to change the likelihood of human error causing a non-conformity.

2. ASQ Quality Press. (2017). The Certified Quality Engineer Handbook (4th ed.).

Chapter 6

"Risk Management

" Section: "Risk Control/Treatment

" p. 81. This section explains that risk control involves implementing risk reduction measures. It states

"Risk reduction focuses on lessening the probability or severity of a risk..." Employee training is a fundamental method for reducing the probability of errors.

3. Stamatis

D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.

Chapter 7

"The FMEA Application

" pp. 169-171. In the context of FMEA

a tool for risk management

"Recommended Actions" are the equivalent of risk treatment. These actions are designed to reduce the Risk Priority Number (RPN) by lowering severity

occurrence

or detection. Actions to lower the "Occurrence" rating frequently include procedural changes and operator training.

A paired-sample t-test is designed to compare the means of two related samples, where each observation in one sample is paired with an observation in the other. The test functions by first calculating the difference for each pair. Then, it effectively performs a one-sample t-test on these calculated differences. The test statistic, t, is computed by taking the mean of the differences (dbar) and dividing it by the standard error of the mean difference. The standard error is the sample standard deviation of the differences (sd) divided by the square root of the number of pairs (n). This procedure and formula are correctly represented by Option C.

A: This formula is for a two-sample t-test assuming equal variances (pooled variance), which is used for independent, not paired, samples.

B: This is the formula for a Welch's t-test, used for comparing two independent samples when their variances are assumed to be unequal.

D: This is the test statistic for a one-sample z-test, which is appropriate only when the population standard deviation (σ) is known, not the sample standard deviation.

1. Montgomery

D. C.

& Runger

G. C. (2018). Applied Statistics and Probability for Engineers (7th ed.). Wiley. In Chapter 10

Section 10-3

"Inference on the Difference in Means of Two Normal Distributions

Paired Observations

" the test statistic for the paired t-test is defined as t0 = dbar / (sd / sqrt(n)) (Equation 10-10).

2. ASQ Quality Management Division. (2017). The Certified Quality Engineer Handbook (4th ed.). ASQ Quality Press. In Chapter 23

"Hypothesis Testing

" the section on "Tests on Means—Paired Data" presents the test statistic as t = dbar / (sd / sqrt(n)).

3. Pennsylvania State University. (n.d.). STAT 500: Applied Statistics. Eberly College of Science. In Lesson 6.2

"The Paired t-test

" the test statistic is explicitly given as t = dbar / (sd / sqrt(n)).