Attribute control charts differ from variable control charts because they:

Question

BlackTom AI · Accepted Answer

The question asks how attribute control charts differ from variable control charts.
Option 1: 'Measure a continuous variable like length or weight.' This describes variable (measurement) charts, which track continuous data, not attribute charts. Attribute charts typically deal with counts or yes/no outcomes, not precise measurements, so this is incorrect.
Option 2: 'Provide more predictive information about future variation.' In general, variable charts tend to give more information about the magnitude of processes and can be more predictive of future performance due to tracking actual measurements. Attribute charts summarize pass/fail outcomes and usually provide less detailed predictive insight, so this claim is not accurate for attributing more predictive power to attribute charts.
Option 3: 'Use smaller sample sizes.' Sample size is not inherently fixed to attribute versus variable charts; both types can use varying sizes depending on the situation. This statement is not a defining difference between the two chart types, making it incorrect as a distinguishing feature.
Option 4: 'Report only whether an item is defective or not.' This aligns with the core distinction: attribute charts (like p-charts or c-charts) summarize data into counts or proportions of defects, effectively answering a yes/no question about each item, whereas variable charts measure a continuous attribute. Therefore, this option correctly captures the key difference.
In summary, the true distinguishing feature is that attribute control charts convey information about defect presence (defective or not) rather than a measured numerical value, which is what variable charts do. The remaining options describe aspects that are more characteristic of variable charts or are not universally true differentiators between the two chart families.

Attribute control charts differ from variable control charts because they:

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