SSGI SixSigma Green Belt: Chapters 20 thru 31
The UCL and LCL can be placed anywhere on the control chart. However, their position will determine the likelihood of the errors that can be made when determining whether or not the process is in-control or out-of-control.
True
When designing an X-Bar chart for continuous data and where the sample size will be 14, the standard deviation is used to determine the UCL and LCL.
True
When determining process capability, the larger is the estimate of the standard deviation of the process, the lower is the process capability.
True
When establishing the UCL and LCL in a P-Chart, and under the assumption that these limits will be set at plus and minus three standard errors of the proportion from the centerline, only the average value for p and sample size n are needed.
True
When process variation is greater, we would expect that the LCL and UCL would be set farther apart.
True
When the sample size is exactly 25, the shortcut and non-shortcut methods will generate very close to the same UCL and LCL.
True
When the sample size is large and discrete data will be collected, a P-Chart is used.
True
While RPN is a quantitative measure, FMEA is primarily a qualitative tool.
True
X-Bar Charts, where sample sizes are less than 12, rely on the range of preliminary samples to establish the UCL and LCL. However, X-bar charts, where sample sizes are between 12 and 25, rely on the standard deviation of preliminary samples to establish these levels.
True
X-Bar and S-Charts are used when the data that is sampled is continuous and the sample size is greater than 12 but less than 25.
True
You are interested in determining whether or not a training program has met expectations. The alternative hypothesis states that it has met expectations.
True
Choosing the right chart depends upon the ___________ .
Type of data, sample size, and type of statistic: All of the above.
The sample size is 25. The mean is 75. The estimate of the standard deviation is 10. What is the LCL?
69
For which of the following is ANOVA appropriate?
A hypothesis test when the means of more than two samples must be compared
The Alpha error is considered too large. It can be made smaller by raising the LCL and lowering the UCL. In other words, it can be made smaller by decreasing the distance between the UCL and LCL on the control chart.
False
The Beta error associated with monitoring and controlling the radiation level in a nuclear reactor plant should be large.
False
The LSL and USL must be contained within the LCL and UCL.
False
The R-Chart is used when monitoring process variation for discrete data.
False
The UCL and LCL are set at plus and minus three standard deviations from the mean or center line of the process control chart.
False
The center line in an R-Chart represents the process mean.
False
The control document needs to be sufficiently complete such that it does not need changes over time.
False
The equation presented in this chapter can be used when the sample size times the average proportion is less than 5.
False
The number of returns received by an online retailer is an example of a situation where a P-Chart would be appropriate.
False
The p-value test is used to determine if the intercept in the regression model is significant.
False
The range is used as an estimate of the standard deviation when sample sizes are greater than 25
False
The sample size is included when calculating the standard deviation of a proportion.
False
The selection of an appropriate chart is not dependent on the size of the sample that is taken from the process.
False
The shortcut method uses the same constant B(3) to calculate both the UCL and LCL.
False
The two types of data that can be monitored include discrete and attribute.
False
The type of chart used in a control process is dependent only on the type of data - continuous or discrete - collected.
False
There are three types of data including continuous, discrete and digital.
False
To compute process capability you must have the UCL and LCL.
False
To determine the mean of a process for the purpose of establishing the center line on the control chart, it is necessary to take only one small sample of process output.
False
To find the Range of a sample, the highest value in the sample is subtracted from the mean of the sample.
False
To find the UCL and LCL for an X-Bar chart when the sample size is less than 12 the standard deviation of the sample must be computed.
False
Using the conventional critical p-value, if the p value of a regression equaition coefficient is 0.0651 it can be considered significant.
False
When a sample is taken from an operational process, the sample mean is plotted on the R-Chart.
False
When designing an X-Bar chart for intermediate size samples, it is necessary to compute an average value of the standard deviation from several preliminary samples to establish a center line for the chart
False
When determining the UCL and LCL for proportions, it is not necessary to consider the size of the sample.
False
When sample sizes are greater than 25, it is still necessary to correct for bias when calculating the UCL and LCL.
False
When setting the UCL and LCL at plus and minus 2 standard deviations from the mean the likelihood of an alpha error is 95%.
False
When the X-Bar chart is completed and then used to monitor process output, only the range is plotted on the control chart.
False
When the proportion is as low as 0.01 or 1 percent, it is not possible to use the equation presented in this chapter for determining control limits.
False
When the regression equation is significant, all of the coefficients of the independent variables will also be significant.
False
When the shortcut is used to create the control chart, the advantage is that the likelihood of incurring an Alpha or Beta error is reduced to zero.
False
X-Bar and R- Charts are used to establish a control system when the sample result is a continuous variable and the sample size is less than 25.
False
X-Bar charts are used for small samples where the data are discrete.
False
Failure Mode and Effects Analysis is primarily used in the inspection process after items have been manufactured. It assures that defective output is not passed on to the customer or client.
False; It is intended to prevent problems from occurring not to correct them once they have occurred.
The difference between what a customer expects and what a process delivers is expressed by the difference between the LSL and USL.
False; The difference is expressed by the distance between the LSL and LCL, and the distance between the USL and UCL
Which of the following is not one of the seven elements of a control plan.
Financial Analysis
The value of "b" in the simple linear regression equation represents _____________________ .
The increase in Y attributed to a unit increase in X
Which of the following is an appropriate null hypothesis for a Two Sample t Test?
The two means come from the same population
A health clinic with four locations administers a patient satisfaction questionnaire across all clinics. The objective is to determine if patient satisfaction differs across these locations. A two-tailed test would be appropriate.
True
A multiple regression model that proves to be significant cannot confirm a causative relationship between the independent and dependent variables.
True
A one-tailed test is used if deviations of the sample mean in only one direction from the target or population needs to be considered.
True
A relationship between the divorce rate in New Jersey and the per capita consumption of coffee in that state has an R-squared of 0.75. This is an example of accidental correlation.
True
A sample of 30 customers rate the quality of a product as satisfactory or unsatisfactory. The data in the sample would be considered discrete.
True
A two-tailed test is used if deviations of the sample mean in either direction from the target or population need to be considered.
True
Alpha and beta errors help to establish where the UCL and LCL will be positioned on the control chart.
True
An R-Chart is used to ensure that the output from a process produces consistent results.
True
An X-Bar and S-Chart can be used for continuous data when sample size is greater than 25.
True
An alternative hypothesis states what it is you are trying to prove.
True
Brainstorming sessions are held to identify problems before they occur.
True
Consider that a P-Chart has been designed and is now ready for use. When entering sample results into the chart, the results will be entered in the form of a percentage. One result for online product return process, for example, would be 15% or 0.15.
True
Customer expectations are expressed using the concept of service levels.
True
Customer satisfaction registered on a scale from one to ten is an example of continuous data. For example, a customer could rate satisfaction as 6.5.
True
Data for the length of time it takes to undergo a routine pre-operative physical in a hospital is an example of continuous data.
True
Discrete data can be summarized as a proportion such as the percent of customers that would recommend a website to a friend.
True
FMEA is a proactive approach that examines what could go wrong with a process, product or service before the design is finalized.
True
If correlation analysis finds that 82 percent of the variation (R-squared = .82) in the dependent variable can be explained by three independent variables, we can say that 18 percent of the variation is unexplained by this model.
True
If process capability is 10, this suggests the process is very capable of meeting customer expectations as expressed by the LSL and USL.
True
If the Alpha error is relatively large, the Beta error will be relatively small.
True
If the calculated p-value is 0.005 as compared to a critical value of 0.01 the null hypothesis should be rejected.
True
If the sample size is 100 and the average value of the proportion is 0.06, the equation presented in this chapter can be used to establish control limits.
True
In a multiple regression model the number of independent variables is always greater than one.
True
In a simple linear regression model, the p-value for the regression line and p-value for the coefficient of the independent variable are the same.
True
In the RPN calculation the letter "O" represents the frequency with which the problem is likely to occur.
True
Monitoring good and defective parts produced by a machine is an example of collecting discrete data.
True
Non-parametric tests do not rely on the assumption of normality in the underlying population distribution.
True
Once the control chart for variation has been designed, the standard deviation of a sample is plotted on the chart.
True
Once the type of control chart has been determined, the center line must be established.
True
One reason why small size samples of less than 25 items or units is taken is because the cost of sampling a larger number would be expensive.
True
Preliminary samples are necessary to determine the center line of the X-Bar chart when sample sizes are between 12 and 25.
True
Preliminary samples are necessary to find the target or center line in a control chart.
True
Process capability depends upon the difference between the USL and LSL.
True
Process capability is determined by three variables; the LSL, USL and standard deviation of the process.
True
Process capability measures how close a process is running to its capability or service limits.
True
Process means, which measure the capability of a process, can not be changed by simply changing the UCL and LCL.
True
R-Charts are used to monitor process variation by plotting the range of items collected in a sample.
True
Rejects in a process are suspected to be related to the average number of hours of training offered to employees. The number of hours of training would be considered the independent variable.
True
Setting the UCL and LCL requires that the costs associated with Beta and Alpha errors be balanced.
True
Suppose it is critically important to determine when a process mean has shifted. In this situation the UCL and LCL should be set such that the Beta error is small.
True
The S-Chart is similar in function to the R-Chart in that it monitors process variation.
True
The Taguchi loss function suggests that the loss to the organization gets larger and larger as process variation migrates farther and farther away from the mean.
True
The purpose of a null hypothesis is to ____________________________ .
state the opposite of what it is you are trying to prove
An X-Bar chart for samples greater than 25 can also be used to plot proportions.
False
An X-Bar chart is used to monitor process means when the data are discrete.
False
An alpha error detects a process change that is not present while a beta error fails to detect a process change that is present.
False
As the sample size decreases, the distance between the UCL and LCL in a P-Chart becomes smaller.
False
Attribute data is the same as Continuous data.
False
Before plotting the mean of a single sample on a control chart, the sample mean must be divided by the square root of the sample size.
False
Both the LSL and USL must be positioned above the Mean (center line) or the process is out-of-control.
False
Consider a process that is in-control; both its mean and variance are performing as expected. If the UCL and LCL have been set at plus or minus 3 standard deviations from the mean. The likelihood of a sample mean falling outside these limits is 1.00 - .997 or .003 percent. Suppose this has occurred. The process is shut down and examined for problems. A beta error has occurred.
False
Data that can be measured on a continuum are called discrete data.
False
Declaring whether or not you will vote in an upcoming election (yes or no) is an example of continuous data.
False
FMEA is a process that attempts to correct process problems once they have occurred.
False
For most processes, the X-bar chart is the only chart needed to monitor process output.
False
If a process mean is monitored, it is not necessary to monitor its variance.
False
If a thorough FMEA effort has been made, there would be no reason to establish a Six-Sigma control process.
False
If the Beta error is relatively small the Alpha error will also be relatively small.
False
If the S-Chart suggests that a process is out-of-control, it will be necessary to confirm this with the X-Bar chart before the conclusion can be made that the process is out-of-control.
False
If the UCL and LCL are moved closer to the center line, the USL and LSL will not be affected.
False
If the X-Bar chart shows that a process is in-control it will not be necessary to review the S-Chart. The conclusion should be made that the process is in-control.
False
In a X-Bar chart where the size of the sample is between 12 and 25, it will not be necessary to take several preliminary samples to establish a center line on the chart since most sample means for samples of this size will be the same.
False
In a multiple linear regression model, the p-value for the regression line and the p-values for all of the coefficients of the independent variables will be the same.
False
In an X-Bar Chart when the sample size is between 12 and 25, the constant D(4) is used to determine both the UCL and LCL.
False
In general, a process capability of 10 or greater is preferred.
False
In multiple linear regression, with four independent variables, it is possible to estimate the position of the regression line using a scatter plot.
False
In multiple regression, at most one independent variable can be significant.
False
It is not necessary to create an S chart when sample sizes are greater than 12.
False
It is not possible for process capability to be greater than 60.
False
It is suspected that sales for a company are related to population density and personal income. Sales is considered to be the independent variable.
False
Non-parametric tests are considered more powerful in differentiating between accepting or rejecting a null hypothesis than a parametric test.
False
Once the X-Bar chart has been designed for samples between 12 and 25, a chart that monitors the variation in process output would not be necessary since the sample size is larger than 12.
False
P-Charts are used for continuous data.
False
P-Charts are used only for small size samples when the data are continuous.
False
P-Charts can also be used to monitor continuous variables.
False
Performance reporting involves preparing a book of control charts showing the performance of the process over time.
False
R-Charts are only used when sample sizes are greater than 12.
False
R-Charts are used to monitor process means.
False
RPN is an abbreviation for Reject Priority Number
False
RPN measures the cost of defectives.
False
An S-Bar chart is used to monitor process variation regardless of the size of the sample.
False
An S-Bar chart is appropriate for monitoring variation when samples of less than 5 items are taken.
False
When a Two Sample t Test is run, the results indicate that the calculated p-value is 0.06. Using the conventional critical p-value, the null hypothesis _____________.
Cannot be rejected
The management plan designed to manage a quality control process is called a _________________ .
Elements of a Control Plan
A process produces a sample mean outside the UCL and LCL but the process mean has not actually shifted. If corrective action is taken this represents a Beta error.
False
A quality control system together with control charts has been set up to monitor the length of time in hours it takes for an order to be shipped to a customer. The type of data monitored in this system would be classified as attribute.
False
Alpha and Beta errors need only be considered when creating charts that monitor means not charts that monitor variation.
False
The value of "a" in the regression equation represents which of the following?
Intercept with the y-axis
Which of the following is the non-parametric equivalent of the Two Sample t Test?
Mann-Whitney
Which of the following is the non-parametric equivalent of the One sample t Test?
One sample sign
Which of the following tests require that the distribution from which samples are taken is normal or close to normally distributed?
One sample t
Which of the following is not a Six Sigma control chart?
Q-chart
Which of the following is an appropriate null hypothesis for an ANOVA test?
Sample means are equal
When Sample sizes are larger than 25 an X-Bar chart is used to monitor process means.
True
The UCL and LCL will be positioned closer to the process mean or center line of the control chart as the estimate of the standard deviation gets smaller.
True
The average range of a sample is needed to compute the UCL and LCL for an X-Bar chart.
True
The average standard deviation of preliminary samples is used as the center line of the S-Chart
True
The average standard deviation of preliminary samples that was computed when establishing the control limits for the X-Bar chart for intermediate size samples can be used as the center line for the S-Chart.
True
The center line for an R chart is determined by taking several samples, determining the range for each of the samples and then computing the average of these ranges.
True
The centerline for a P-Chart would be determined by taking several preliminary samples, determining the proportion for each sample group, and finally computing the average of these group proportions.
True
The computed p-value from a Two Sample t Test measures how likely it would be to observe two samples whose means are that far apart.
True
The consequence of raising the UCL and lowering the LCL is that the Alpha error will decrease.
True
The distribution of all samples that are taken and positioned on an X-Bar chart, when the sample sizes are larger than 25, will all fall in a symmetrical or bell-shaped pattern around the center line of the chart.
True
The first decision when selecting an appropriate control chart is whether or not the data are continuous or discrete.
True
The long time average of rejects from a process is 2%. When constructing a P-Chart, and assuming that the process has not changed, the 2% could be used as the centerline of the control chart.
True
The mean from a process has shifted but a sample result falls within the UCL and LCL, This is an example of a Beta error.
True
The mean of a sample is plotted on the X-Bar chart when sample sizes are greater than 25.
True
The mean of the means is called the "grand mean" and is used to establish the center line in an X-Bar Chart.
True
The median statistic is used in non-parametric statistics because the median is less sensitive to extreme values found in a highly skewed distribution. So it is most appropriate for small sample sizes.
True
The more difficult it is to detect a problem the higher will be its RPN.
True
The number "3" in the formula, used to determine the UCL and LCL, represents the setting of these limits at three standard errors of the proportion on either side of the centerline.
True
The objective behind the construction and use of an R chart is to determine when the variation of a process is higher than expected.
True
The process mean is 35. Sample size is 25. The average standard deviation is 5. The value of A3 is 0.606. The UCL will be 38.
True
The range in an R-Chart is used to approximate the variation in sample means from one sample to the next.
True
The response plan identifies the steps that need to be taken should a problem occur when monitoring process performance.
True
The shortcut formula to determine the UCL and LCL on an X-Bar chart uses the constant A(2) as well as the range to establish their distance from the center line.
True
The standard error of the proportion is similar in concept to the standard error of the mean.
True
The terms discrete and attribute data are used interchangeable
True
The values D(4) and D(3) are used when determining the UCL and LCL for R-Charts.
True
There is a difference between the quality of what customers expect and the capabilities of the process that delivers these products and services.
True
To detect even the smallest shift in a process mean it would be necessary to set the UCL and LCL close to the process mean.
True
To determine the center line for X-Bar charts, when the sample size is between 12 and 25, the grand mean of preliminary samples is calculated.
True
To determine the center line or target of a control chart, several samples must be taken, then the mean of each sample computed, and finally the mean of these means calculated.
True
We can conclude from the shortcut formula that the range of the sample is used in estimating the standard error of the mean.
True
Non-parametric tests are necessary when _________________________________ .
all of the above; sample sizes are small normality of the distribution cannot be assumed the distribution from which the sample has been taken is highly skewed.
You need to determine whether or not a process has met its objectives. A sample is taken and must be compared to a target. Which of the following tests is most appropriate? Assume normality and large sample size.
one sample t test
