# Types of Data

Types of Data- Data is the backbone of any six sigma project as the whole idea of six sigma and operations is to use statistics to manage operations in the factory workshop. That is why for a six sigma team to understand the types of data and when and how to use them is of vital importance. Here are the types of data that are used for statistical analysis:

• Continuous Data: Continuous data is of the type that must be measured against the type that we can count. Consider for example length of an object as a data type. The length of an object can be between 1 foot and 2 feet, it can be 1.5 feet, it can even be 1.54 or 1.546 feet depending upon the number of decimals and the degree of precision that has been decided in the data collection plan.
• Discrete Data: Discrete data is the data that needs to be counted as opposed to being measured.

Here the values fall in one of the categories.

• Binary: The data in such cases need to be entered in one of the two categories like true or false, applicable to six sigma analyses or not applicable to six sigma analysis. Many times the outcomes of success and failure determine the operations of the shop floor. It is in such cases that the binary discrete category comes in handy.
• Ordered Categories: The data in these cases needs to be entered in one of the multiple categories that are ranked. Here there may be more than two categories. In fact there usually are more than two categories involved. The categories may be based on the relative importance or on some type of number scale.
• Unordered Categories: The data in these cases is entered in one of the multiple categories that need not be ranked. There are usually more than 2 categories. Data lying in one category is usually no different from data lying in any other category.
• Count: This is a simple counting of data without any categorization involved. This represents the discrete variables in its truest form but is rarely used in the six sigma process because it does not provide much analytical insight into the variables being studied.

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