As we continue our journey in Six Sigma it seems pertinent to discuss the different types of distributions you will see in your analysis. Let’s start with one at a time. The most common distribution is the Normal Distribution and here’s what you should know about it.
First, what is a distribution?
Simply put, a distribution will tell you how often a variable occurs in your process. This is important because the commonness of your variables will inevitable create a foundation for your improvement project.
Types of Distribution
The Normal Distribution
A normal distribution (Gaussian Curve, the average person knows it as the Bell Curve) shows a equal distribution. The mean (the average) divides the data in half, 50% on the data on each side of the mean. The Normal Distribution will have the following hallmarks:
This distribution is considered to be the most important distribution.
The area under the curve should equal 1.
Physical aspects of the curve should resemble a hill and should be symmetrical.
Both directions on either side of the mean extend indefinitely and never touch the horizontal axis.
White noise in your process should produce a normal curve shape
The Z distribution has a mean of 0 and a standard deviation of 1.
The mean (average), median (mid-point) and the mode (most common value) should be the same data value.
Next week, it’s on to non-normal classifications. Get to analyzing and if you need any help, reach out and let us know!
This blog is about Six Sigma data analysis. Because statistics are such a big part of the Six Sigma world, it makes sense that we talk about the data that is gathered and what it means. So here we go….
There are different types of data and anytime you measure something you going to need how to interpret it. There are two main types of data: attributive and variable.
Some people call this the most basic form of data, but for business purposes I don’t accept that. Qualitative data is simple in the fact that it is generally data that can be gathered by asking a yes or no question. For example, ‘Did they buy the new product?’ What is limiting about attributive data is that you really can’t analyze the results in a meaningful way, but it can give you a pretty good place to set your focus.
Variable data is also called quantitative and this is the data that you can measure and analyze. In order to decide if data you have is variable ask yourself these questions:
- Can you classify the data and count the results? (Think number of defects for a particular product line)? If you can this is called discrete data and the limitation of discrete data is that it cannot be broken down into smaller measurements to create additional meaning. It’s a one hit wonder.
- Can the data be measured on a time line with meaningful divisions (Think time, production speed, delivery dates etc…) If you can this is called continuous data and it can be divided further to create additional data.
As with all of these blogs, this is to get you started and statistical data clearly has more to it than one paragraph. But information is the first step and one you know what type of data you have, you have a better idea of what you need to know. Give us call and we can help you create where you need to go next.