Types of Data

Data describe the characteristics of products, processes, or services. These characteristics can be divided into qualitative and quantitative data. For data-based analysis in Lean Six Sigma, it is important to be familiar with the following types of data and distributions..
Objective
  • Understanding data
  • Understanding distributions
  • Use the right diagrams
  • Compare results accurately
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Procedure:

Determining the characteristic

Determine the characteristic and its attributes. For example, characteristic = age; attribute = 42 years.

Categorization of attributes

The attribute values must be placed on a scale to define the data type.

SkalaMerkmalausprägungenBeispiele
NominalRein qualitativ ohne OrdnungGeschlecht, Berufsstand, Farben
OrdinalQualitativ mit natürlicher OrdnungNoten, Rang beim Militär
MetrischQuantitativ (zählbar/messbar)10 Fehler, Luftdruck, Temperatur

Analysis and interpretation of the data

After identifying the data, it can be analyzed using statistical methods.

The following illustration can be used to correctly categorize the data.

Datentypen - 1

In statistics, distributions are typically described by functions. There are models (shapes) for both continuous and discrete distributions.

Binomialverteilung
The binomial distribution is a discrete distribution that represents the frequency of characteristics with two possible outcomes. For example, it can be used to model the number of defects in a production process.
Poissonverteilung
The poisson distribution is a special case of the binomial distribution. It describes the number of errors per unit that occur within a given unit.
Hypergeometrische Verteilung
The hypergeometric distribution is used when samples are drawn from small populations without replacement. An example is lottery numbers.
Normalverteilung
The normal distribution is the most important distribution function for continuous data. Most statistical methods are based on normally distributed data.
Lognormalverteilung

The log-normal distribution is steep on the left and right-skewed, and it does not take values less than 0. It is often used to describe process times.

Weibullverteilung
The Weibull distribution is very flexible in its shape. Through the parameter values, the curve can be adjusted to fit various patterns. Examples include lifetime analyses.

With more than 4,000 projects and case studies, Alphadi is a leader in the field of Lean Six Sigma and Sales Process Engineering. We advise you holistically so that your goals are achieved sustainably and in the long term.

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