Compared to attribute control charts, variable control charts are more informative because they:

Question

BlackTom AI · Accepted Answer

When comparing attribute control charts to variable control charts, the key distinction lies in the type of data and the level of detail they provide.
Option 1: 'Measure a continuous characteristic and provide more detailed information' is accurate because variable charts track measurements that can take on a range of values (like length, weight, temperature), yielding richer, quantitative insight such as means, variances, and trends over time.
Option 2: 'Only show whether a unit is defective' describes attribute charts, not variable charts. Attribute charts classify items as defective or non-defective, which is a binary assessment and yields less granular information than measurements.
Option 3: 'Require smaller sample sizes' is not generally true. Variable charts often require sufficient sample sizes to estimate process parameters like the mean and standard deviation; in some cases they may even require larger samples to achieve the same sensitivity, depending on the method used.
Option 4: 'Automatically detect the root cause of defects' is beyond the scope of control charts themselves. Control charts monitor process stability and identify signals or out-of-control conditions, but diagnosing root causes typically requires additional analysis (like cause-and-effect tools or regression studies).
Overall, the essence of the comparison is that measuring a continuous characteristic provides more detailed information about the process, which makes variable charts more informative in that sense.

Compared to attribute control charts, variable control charts are more informative because they:

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