Attributes Data Analysis

Attributes Data Analysis


Attributes Data Analysis- Six Sigma is a very successful methodology used to provide companies with a wealth of information that will help them improve their overall quality. Attribute data is one statistical aspect that is widely used and very important to the future success of a company.

The success of the Six Sigma Methodology is highly reliant on statistical analysis. One statistical concept is that of attribute data. Any measurements that are not quantified on an infinitely divisible numeric scale are considered to be discrete or attribute data. This includes items such as:

  • counts,
  • proportions,
  • ratios, or
  • the percentage of a characteristic.

Such characteristics can include everything from loan forms to department attendance. This can possess measurements like pass or fail, leak or no leak, medium or large, or go or no go tests. 

Attributes data may be used with control charts such as p charts, or percent defective charts along with various others. This is the opposite of variable data where real numbers are found in the measurements. This means data that can be measured on a continuous scale.

Attributes data is important to a business’ success and can be used to make a wide variety of determinations. The ability to measure data that does not necessarily show up in real numbers gives company owners a more controlled way to keep up with important events and information pertaining to both their employees and the company as a whole. This can also include customer satisfaction as some factors of this may also be presented in this way.

Attributes data is one version of analyzing different information and enables company owners and team members to measure data that could otherwise seem immeasurable. Understanding it is important where utilizing Six Sigma methods is concerned, and the presence of 6 Sigma makes the results much easier to ascertain. The charts also make it easier to understand the data which, in turn, places answers to important questions in full view where they may be analyzed and processed in a way that will make reaching the end result easier for everyone involved.

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