Continuous data is also called variable data, quantitative data or measuring data. For example, physical measurements such as temperature and height, and amounts of money if fractional units are allowed. Discrete data does not have a continuous range of values, but is limited to set values and can be counted.
For Six Sigma, discrete data includes: count data (e.g. for counting defects per unit: uses Poisson statistics) and attribute data (usually binary yes/no for classifying e.g. defective/not defective, pass/fail: attribute statistics use the binomial distribution).
Some Six Sigma workers use the term attribute data to include categorical and discrete data. Categorical data (also called nominal data) sorts items into non-overlapping groups which have no natural order e.g. red, yellow, blue; wood, metal, plastic; postcodes & zip codes.
Ordinal data is discrete data that has an order e.g. 1st, 2nd and 3rd in a race; rating of good, middling, bad in a customer survey.
Continuous data is also called variable data, quantitative data or measuring data. For example, physical measurements such as temperature and height, and amounts of money if fractional units are allowed. Discrete data does not have a continuous range of values, but is limited to set values and can be counted.
For Six Sigma, discrete data includes: count data (e.g. for counting defects per unit: uses Poisson statistics) and attribute data (usually binary yes/no for classifying e.g. defective/not defective, pass/fail: attribute statistics use the binomial distribution).
Some Six Sigma workers use the term attribute data to include categorical and discrete data. Categorical data (also called nominal data) sorts items into non-overlapping groups which have no natural order e.g. red, yellow, blue; wood, metal, plastic; postcodes & zip codes.
Ordinal data is discrete data that has an order e.g. 1st, 2nd and 3rd in a race; rating of good, middling, bad in a customer survey.
BPMN