FREE Lean Six Sigma Green Belt Knowledge Questions and Answers
The runs of your data collection for measurement system analysis should ideally be randomized.
When conducting a Measurement System Analysis (MSA) or any experimental study, randomizing the runs of data collection is an important practice.
Randomization is the process of assigning the experimental conditions or data collection order in a random or haphazard manner. It helps minimize the impact of potential confounding factors or biases that could influence the results of the study.
When assessing a data set's dispersion, average moving range is the preferable statistic above standard deviation.
The statement is false. The average moving range is not the preferred statistic over standard deviation when evaluating the dispersion of a data set.
When evaluating the dispersion or variability of a data set, the standard deviation is typically the preferred measure. The standard deviation is a statistical measure that quantifies the amount of variation or spread in a data set. It represents the average amount of deviation or dispersion of data points from the mean.
This kind of information is frequently used in visual inspections to quantify problems with acceptance criteria.
Visual inspections that quantify defects with acceptance criteria typically use Attribute data.
In the context of data types in quality control and process improvement, data can be categorized as either Attribute data or Variable data. Attribute data is qualitative data that represents the presence or absence of a certain characteristic or attribute, and it is typically expressed in terms of counts or percentages. On the other hand, Variable data is quantitative data that represents a measurable quantity or value.
What else is the term "Poka-Yoke" known as?
The term "Poka-Yoke" is also known as Mistake-Proofing or Error Avoidance.
Poka-Yoke is a Japanese term that means "mistake-proofing" or "error avoidance." It is a quality improvement technique used to prevent defects or errors from occurring during the manufacturing or process execution. The main objective of Poka-Yoke is to design processes and systems in a way that makes it nearly impossible for errors to happen or, if they do occur, easily detectable and correctable.
What kind of control charts can I use when I have attribute data?
U (unit) or P (proportion) charts are acceptable for use with attribute data.
In Statistical Process Control (SPC), attribute data refers to data that can be categorized into discrete categories or attributes, typically represented by counts or proportions. Attribute data is qualitative data that represents the presence or absence of a certain characteristic or attribute.
By employing the same measuring tool or the same measuring procedure to measure the exact same characteristic of the same part or object, what is meant by repeatability? (Also called the "within" variation)
Repeatability, also known as "within" variation, refers to the variation in measurements that occur when the same operator uses the same measuring device or the same measuring process to measure the identical characteristic of the same part or item multiple times.
The "Funnel Effect" is the practice of narrowing the project team's attention to only one issue.
The statement that the "Funnel Effect refers to reducing people on the project team to focus on only one problem" is false.
The "Funnel Effect" is a term used in the context of Lean Six Sigma to describe the phenomenon where the number of potential solutions or ideas gradually narrows down or funnels as a project progresses through various stages of improvement. It does not refer to reducing people on the project team; instead, it pertains to the reduction of potential solutions.
What is a C&E matrix used for primarily?
The main purpose of a C&E (Cause and Effect) matrix is to use the knowledge of a group or team to determine the critical few Xs (causes or factors) that have the most significant impact on a specific problem or effect.
A C&E matrix, also known as an Ishikawa diagram or fishbone diagram, is a visual tool used in problem-solving and root cause analysis. It helps teams systematically identify and organize potential causes or factors that may contribute to a particular effect or problem. The diagram gets its name from its appearance, which resembles the skeleton of a fish, with the effect (problem) represented as the "fish head" and the potential causes branching out like "fishbones."
Which of the following is not a typical way to identify waste?
Recycling is not a standard form of waste identification in the context of waste management and waste classification. Recycling is a waste management strategy, but it is not a specific form of waste identification.
The "Rs" in Gauge R&R stand for repeatability and reproducibility in relation to MSAs.
In the context of Measurement System Analysis (MSA) and Gauge R&R (Repeatability and Reproducibility) studies, the "Rs" indeed stand for "Repeatability" and "Reproducibility."
Gauge R&R is a statistical technique used to assess the variation and reliability of a measurement system. It helps determine how much of the total variation in the measurement process is due to the repeatability (measurement error within the same appraiser or instrument) and reproducibility (measurement error between different appraisers or instruments).
What does the C&E in a fishbone diagram stand for?
"C&E" in a fishbone diagram is indeed short for "Cause and Effect."
A fishbone diagram, also known as an Ishikawa diagram or a cause-and-effect diagram, is a graphical tool used to identify and organize the potential causes of a specific problem or effect. It is a helpful technique in root cause analysis and problem-solving.
The format and functionality of Minitab worksheets are exactly the same as those of an Excel worksheet.
The statement is false.
Minitab worksheets and Excel worksheets are not the same and do not function in the exact same way. While both software tools are used for data analysis and manipulation, they have distinct features and purposes.
If you were the patient undergoing surgery, you would want the surgeon to have a low alpha risk. This would give you a high level of confidence that any defects would be found and corrected before you, the patient, received the procedure.
As a customer in a surgical process, you would want the surgeon to have a low alpha risk, which equates to a high confidence that any defective or faulty outcomes are caught before they are passed along to you.
Is it true that the client specifies the standard deviations lines above and below the process data on control charts rather than the data's variation?
The statement is false. In control charts, the standard deviation lines above and below the process data are not specified by the customer; rather, they are typically calculated from the variation in the data.
Control charts are graphical tools used in statistical process control (SPC) to monitor the stability and performance of a process over time. These charts consist of a central line that represents the process mean (often denoted as the "X-bar" or "average") and control limits that surround the central line. The control limits are usually set at a certain number of standard deviations from the process mean.
Which chart should be analyzed first when analyzing an X-bar and Range chart? X-bar or Range charts are used.
When interpreting X-bar and Range (X-bar-R) charts, the Range chart should be evaluated first before the X-bar chart.
The X-bar chart and the Range chart are commonly used together in Statistical Process Control (SPC) to monitor the stability and variation of a process over time. The X-bar chart tracks the average or central tendency of a process, while the Range chart monitors the dispersion or variability within subgroups (samples) of data.
In process mapping, box plots are an effective technique for revealing hidden factories.
Box plots are not typically used for showing hidden factories in process mapping. Box plots, also known as box-and-whisker plots, are graphical tools used for summarizing and visualizing the distribution of numerical data. They display the median, quartiles, and any potential outliers in a dataset.