![]() ![]() The process of calculating discrete data is similar to creating it. The table will show how many times each possible value occurs in the dataset. ![]() Once you have those values, you can use them to create a table of discrete values. You can use this information to find out the minimum and maximum values for your discrete data. A histogram shows how many times each value appears in your dataset. One way to do this is by using a histogram (or frequency distribution). To calculate discrete data, you must first identify the range of values that might be present in your data. This can be done manually, as in a tally, or through computer software that counts the number of events and displays that number on screen. ![]() How to Create and Calculate Discrete Dataĭiscrete data is both calculated and created.ĭiscrete data is created by counting the number of times an event happens. It is also easier to analyze discrete data because it does not have as much variation, making it easier for you to make predictions about future outcomes based on past results. Because of this, it is often used when you want to compare two things that are different from each other, such as age or gender.ĭiscrete data provides a good understanding of the relationships between variables. It allows you to break down your results so that they’re easy to understand and compare with each other. ![]() For example, when you are trying to improve your production line efficiency by reducing the number of defects per unit produced, you can simply count how many defects there are and measure that against your goal of zero defects per unit.ĭiscrete data is accurate and not very prone to error. This makes it easy to determine if a process is improving or not, because there is no ambiguity about whether or not a discrete element has passed or failed. As a result, it is easily classified into one of two groups: pass or fail. The Benefits of Discrete Dataĭiscrete data is easy to identify, manage, and analyze. each group) in discrete utilizes counting procedures to gain results and can also be used to determine variances in processes for statistical reasons. What is Discrete Data?ĭiscrete data is a type of qualitative data that is defined by distinct points and can therefore only be represented numerically. They are both important for different reasons and allow for different types of analysis.īecause data is everywhere in businesses built on processes, it is critical that you understand the difference between these two so that data is corrected correctly, and used effectively. Discrete and continuous data are two of the main types of data that you will encounter in LSS. There are different types of data, and in a Lean Six Sigma framework especially, the distinctions are important to make because specific types require specific methods of analysis. Accurately collected and analyzed data is almost as good as a conversation between you and your customer, for it tells you what it wants or needs. The ability to interpret what the data is saying is how you know whether you are on the right path in achieving your goals and objectives and avoiding roadblocks on your journey towards success. When it comes to Six Sigma, data is your lifeblood. ![]()
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