Six Sigma DMAIC Process - Measure Phase - Variation / Discrete vs Continuous Thinking
Discrete/attribute thinking: Was the payment posted on time? (Yes/No.) Was the payment amount correct? (Yes/No.) These are binary categorical outcomes; counting them produces discrete numerical data.Quantitative thinking: When was the payment posted? Record elapsed hours from a defined reference. How much payment was applied? Record the transaction value. Time can be modeled continuously; money has currency increments, though a continuous approximation may suit some analyses.
Examples of categorical and quantitative data:
- Email response time: continuous duration.
- Customer satisfaction score: an individual ordered rating is ordinal. Treating a numerical composite score as continuous requires justified scale and model assumptions.
- Talk time: continuous duration.
- Fatal Accuracy: define what this metric records. A per-call yes/no outcome is binary; a percentage from a fixed number of calls has discrete increments. A continuous approximation needs justification.
- Survey rating: an ordered categorical rating is ordinal; numeric category codes do not establish equal distances.
- Number of calls: a discrete numerical count.
- Number of agents: a discrete numerical count.
Summarize quantitative data using measures of central tendency (location) and spread (variation). The diagram shows mean, median, mode, range and standard deviation.
Quantitative Data Characteristics
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