Try to answer these 30+ Quality Improvement and Control Tools MCQs and check your understanding of the Quality Improvement and Control Tools subject. Scroll down and let's begin!
A. Attribute
B. Taguchi-loss function
C. Histogram
D. Barchart diagram
A. Monitor
B. Ignore.
C. Neglected
D. None of these
A. Variations in quality that arise from random natural differences
B. A control chart to monitor if the average value of the process variable is around target
C. Theorem that states that as the size of a sample gets larger and larger, its distribution also approaches the normal distribution
D. None of these
A. Random natural differences
B. Artificial differences
C. Both a & b
D. None of these
A. Quality
B. Time
C. Value
D. Strategy
A. Parameter design
B. Mean chart
C. Tolerance limits
D. Design specification limits
A. Average value
B. Percentage value
C. Mode value
D. Remaining value
A. Experimental designs
B. Optimal design
C. Branding design
D. None of these
A. P-diagram
B. Process capability index
C. P-chart
D. All of these
A. Design & performance
B. Compatible & condiciones
C. Factor & Effective
D. Both b & c
A. A set of procedures to improve a product’s quality or a service’s quality before it is delivered
B. A measure of how well a process meets its specification limits
C. A process to monitor quality of a product or service after it is produced or delivered
D. All of these
A. True
B. False
A. Constancy
B. Soundness
C. Variability
D. None of these
A. The sampling distribution of the mean will have the same standard deviation as the original population from which the samples were drawn
B. The sampling distribution of the mean will have the same mean as the original population from which the samples were drawn
C. The sampling distribution of the mean will be normal if the original population from which the samples were drawn is normally distributed
D. Sample data are used as a basis from which to make probability statements about the true (but unknown) value of the population mean or proportion
E. Using information from a sample to reach conclusions about the population from which it was drawn is referred to as inferential statistics
A. Special cause variations
B. Tolerances
C. Variable
D. None of these
A. Continuous
B. Discontinuous
C. Statement Incorrect
D. None of these
A. Attribute
B. Variable
C. Identity
D. Special cause variations
A. Lower control limits
B. Upper control limits
C. Center line
D. Variations
A. Design specification limits
B. Errors
C. Variations
D. Attributes
A. Quality control
B. Quality assurance
C. Check sheet
D. Quality management
A. Common cause variation
B. Parameter design
C. Design specification limits
D. Central limit theorem
A. Common cause variation
B. Parameter design
C. Design specification limits
D. Central limit theorem
A. Ten
B. Thirty
C. Twenty
D. Twenty-five
A. Range chart
B. Mean chart
C. C-chart
D. P-chart
A. C-chart
B. P-chart
C. Range chart
D. Mean chart
A. P-chart
B. R-chart
C. C-chart
D. Mean chart
A. Scatter diagram
B. Histogram
C. Pareto charts
D. Check sheets
A. Process capability analysis
B. P-diagram
C. Parameter design
D. Design specification limits
A. Mean chart
B. Range chart
C. C-chart
D. P-chart
A. Process capability analysis
B. Quality control
C. Six Sigma
D. Quality management
A. Mean chart
B. R-chart
C. P-chart
D. C-chart
A. Control charts
B. Quality control
C. Check sheets
D. Process flowcharts
A. Process analysis
B. Data collection
C. Process control
D. Process mapping