Free Practice Questions for ASQ CQE Exam

The correct test statistic for a paired-sample t test is used when comparing the means of two related groups. The formula for the paired-sample t test is:

t=dˉsd/nt = \frac{\bar{d}}{s_d / \sqrt{n}}t=sd​/n​dˉ​

Where:

dˉ\bar{d}dˉ is the mean of the differences between the paired observations.

sds_dsd​ is the standard deviation of the differences.

nnn is the number of pairs.

The client, or the party commissioning the audit, has the primary responsibility for defining the purpose and scope of the audit.

The client sets the objectives, scope, and criteria to ensure the audit meets their specific needs and requirements.

The audit manager and auditor may assist in refining the scope and purpose but the initial determination lies with the client.

References: The ASQ "Auditing Handbook" and ISO 19011:2018 guidelines on auditing management systems outline the client's role in defining audit scope and objectives.

The critical path is the longest path through the project, determining the shortest time to complete the project. The components and their dependencies are:

W: Installation Time = 4 weeks

X: Acquisition Time = 5 weeks + Installation Time = 4 weeks, Total = 9 weeks

Y: Predecessors are W, X, Z. Therefore, Y cannot start until W, X, and Z are completed.

Z: Installation Time = 6 weeks

To find the critical path:

W: 4 weeks

X: 9 weeks

Z: 6 weeks

Y depends on W, X, and Z, so Y starts after the longest time among W, X, and Z, which is 9 weeks (X), and Y takes 4 weeks. Thus, the total time is 9 weeks + 4 weeks = 13 weeks.

References:

Kerzner, H. (2017). Project Management: A Systems Approach to Planning, Scheduling, and Controlling. Wiley.

PMBOK® Guide (2017). Project Management Institute.

The correlation coefficient measures the strength and direction of a linear relationship between two variables. A correlation coefficient of -1.0 indicates a perfect negative correlation, meaning that as one variable increases, the other decreases.

References:

Moore, D.S., McCabe, G.P., & Craig, B.A. (2017). Introduction to the Practice of Statistics. W.H. Freeman.

Montgomery, D.C. (2009). Introduction to Statistical Quality Control. Wiley.

When developing materials for a quality training program, the most effective to least effective methods are:

Job related examples (3)

Case studies (1)

General examples (2)

Theory (4)

This ranking emphasizes practical, relevant, and specific training content over more theoretical or generalized examples.

Calculate the degrees of freedom for error:dfError=Total df−Treatment df=24−4=20\text{df}_{\text{Error}} = \text{Total df} - \text{Treatment df} = 24 - 4 = 20dfError​=Total df−Treatment df=24−4=20

Calculate the sum of squares for error:ssError=Total ss−Treatment ss=643−333=310\text{ss}_{\text{Error}} = \text{Total ss} - \text{Treatment ss} = 643 - 333 = 310ssError​=Total ss−Treatment ss=643−333=310

Calculate the mean squares:

MSTreatment=ssTreatmentdfTreatment=3334=83.25\text{MS}_{\text{Treatment}} = \frac{\text{ss}_{\text{Treatment}}}{\text{df}_{\text{Treatment}}} = \frac{333}{4} = 83.25MSTreatment​=dfTreatment​ssTreatment​​=4333​=83.25 MSError=ssErrordfError=31020=15.5\text{MS}_{\text{Error}} = \frac{\text{ss}_{\text{Error}}}{\text{df}_{\text{Error}}} = \frac{310}{20} = 15.5MSError​=dfError​ssError​​=20310​=15.5

Calculate the F-statistic:

F=MSTreatmentMSError=83.2515.5≈5.37F = \frac{\text{MS}_{\text{Treatment}}}{\text{MS}_{\text{Error}}} = \frac{83.25}{15.5} \approx 5.37F=MSError​MSTreatment​​=15.583.25​≈5.37

The precision of the Coordinate Measuring Machine (CMM) is estimated by its standard deviation in repeated measurements, which is given as 2.497 mm. This value represents the precision of the measurements as it reflects the spread or variability in the measurement data.References:

Introduction to Statistical Quality Control by Douglas C. Montgomery.

ASQ Quality Press: The Certified Quality Engineer Handbook.

If a value assigned to a unit is non-numerical, it is described as qualitative. Qualitative data describes attributes or characteristics that do not have a numerical value. Examples include descriptions such as color, type, or category.

References:

Montgomery, D. C. (2017). Design and Analysis of Experiments. John Wiley & Sons.

ASQ (American Society for Quality) - Types of Data: ASQ Glossary