Data Collection & Analysis Methods Flashcards
7 cards from real CQIA practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Data Collection & Analysis Methods flashcards as text
Which of the following scenarios represents attribute data rather than variable data?
Answer: Classifying invoices as correct or incorrect
Attribute data classifies items into categories (pass/fail, correct/incorrect) rather than measuring on a continuous scale.
A team notices their process data forms a skewed distribution with a long right tail. Which measure of center BEST represents the typical value?
Answer: Median, because it is less affected by the tail
In a skewed distribution, extreme values in the tail pull the mean away from the center, making the median a better representative.
What is the purpose of a gage R&R study in quality improvement?
Answer: To evaluate whether the measurement system contributes excessive variation
A gage repeatability and reproducibility study quantifies how much of total variation is attributable to the measurement system itself.
On a control chart, what does an upward trend of six or more consecutive increasing points indicate?
Answer: A potential special cause drifting the process upward
Six or more consecutive points moving in one direction is a trend signal indicating a possible special cause like tool wear.
When should a team use a c-chart instead of a u-chart?
Answer: When subgroup sizes are constant and defects per unit are counted
A c-chart is used when the number of defects is counted in constant-size inspection units, unlike the u-chart which adjusts for varying sizes.
Which of the following BEST describes the difference between accuracy and precision in measurement?
Answer: Accuracy is closeness to true value; precision is consistency of repeated measures
Accuracy reflects how close measurements are to the true value, while precision reflects how consistently repeated measurements agree with each other.
A team collects warranty return data broken down by product line, region, and quarter. Analyzing subsets of this data separately is an example of:
Answer: Stratification
Stratification separates combined data into subgroups by categories such as product line or region to reveal hidden patterns.