Brief Summary
This video explains how to interpret different histogram shapes to gain meaningful insights from data, emphasizing its importance in the measure phase of DMAIC. It covers various distribution patterns, including normal, skewed, bimodal, plateau, edge peak, QAM, truncated, and dog food distributions, detailing their characteristics and potential causes. The video also briefly touches on normality tests using Minitab and encourages viewers to provide feedback for future content.
- Explains the importance of understanding histogram shapes for data analysis.
- Describes various distribution patterns and their potential causes.
- Briefly discusses normality tests using Minitab.
The video introduces the importance of histogram analysis for understanding variable data behavior. It emphasizes that before drawing conclusions from a histogram, it's crucial to ensure the process was operating normally during the studied period. Unusual events can affect the histogram's shape, making generalizations unreliable. The video aims to explain the significance of interpreting histogram shapes, highlighting that it's not just for publishing reports but also for guiding the thought process in the measure phase of DMAIC.
Normal distribution, characterized by a bell-shaped curve, has several key features. It is symmetrical around its mean, with the mean, median, and mode being equal. The area under the normal curve is equal to 1, and the distribution is denser in the center with less density at the tails. Defined by the mean and standard deviation, approximately 68% of the area falls within one standard deviation of the mean, and about 95% within two standard deviations. Normality tests can be performed in Minitab to assess if data comes from a normally distributed population, using tools like the Anderson-Darling statistic to compare the fit of different distributions.
A skewed distribution is asymmetrical due to a natural limit preventing outcomes on one side. The distribution peak is off-centered toward the limit, and the tail stretches away from it. For instance, the analysis of a very pure product would be skewed because the product cannot exceed 100% purity. Similarly, call handling times cannot be less than zero. These distributions are classified as right or left skewed based on the tail's direction.
A bimodal distribution resembles the back of a two-humped camel, resulting from combining outcomes of two processes with different distributions into one dataset. For example, production data from two shifts might exhibit a bimodal distribution, with each shift producing a different distribution of results. Stratification can often reveal this issue.
The plateau distribution, also known as a multi-modal distribution, occurs when several processes with normal distributions are combined. The presence of multiple peaks close together causes the top of the distribution to resemble a plateau.
An edge peak distribution resembles a normal distribution but features a large peak at one tail. This is often due to faulty histogram construction where data is lumped together into a group labeled "greater than."
In a QAM distribution, the bars alternate between tall and short. This pattern often arises from rounded-off data and an incorrectly constructed histogram. For example, temperature data rounded to the nearest 0.2 degree would display a QAM shape if the histogram's bar width were 0.1 degree.
A truncated distribution resembles a normal distribution with the tails cut off. This occurs when a supplier produces a normal distribution of material but uses inspection to separate what is within specification limits from what is out of specification. The resulting shipment to the customer, containing only items within specifications, exhibits the hard cut shape.
The dog food distribution shows missing results near the average. In this scenario, one customer receives a hard cut, while another is left with the "dog food," which consists of odds and ends left over after the master meal. Although what the customer receives is within specifications, the product falls into two clusters, one near the upper specification limit and one near the lower specification limit, often causing problems in the customer's process.
The video concludes by mentioning that the histogram tool will be further explained in the context of capability analysis during future videos on DMeq learning. Viewers are encouraged to like the videos, provide comments, and suggest topics for future content to make the forum more interactive. The presenter also asks viewers to share the videos and subscribe to the channel, clicking the bell icon and selecting "Get All Notifications" to stay updated on new videos. The presenter commits to sharing useful information to improve viewers' knowledge and skills in continuous improvement.
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Transcript
Full transcript of “Histogram (PART-2) : Different shapes of Histogram and its interpretation (Meaning)”. Click a timestamp to open the video at that moment. Need another video? Use the YouTube transcript generator.
0:00 Hello Friends, The basic histogram concept to study the behavior of variable data we had seen in the last video. We had also seen the procedure regarding how to create histogram as well as how to interpret it. An analysis of the histogram is really tricky and explains you many meaningful things from the collected data. And those things make this tool as a very important tool. Before drawing any conclusions from the histogram, satisfy yourself that the process was operating normally during the time period being studied. If any unusual events affect the process during the time period of the histogram, your analysis of the histogram shape probably cannot be generalized to all time periods. Therefore, analysis the meaning of the histogram shape is very important, which I am going to explain in this video. Some of my friends told that this tool is used to publish reports only.
0:50 Yes, this is the most important use of histogram, but it also gives a correct direction to our thought process which is very critical in the measure phase of the DMEG. So let's begin to learn next interesting things related to histogram. Let's see first the different shapes of the histogram and their meaning. Normal distribution A common pattern is a bell shaped curve. There are 7 features of the normal distributions. Let's see one by one. 1. Normal distributions are symmetric around their mean. 2. The mean, median and mode of a normal distribution are equal. 3. the area under the normal curve is equal to 1. Normal distributions are denser in the center and less dense at the tails.
1:39 Normal distributions are defined by two parameters, the mean and the standard deviation. 68% of the area of a normal distribution is within one standard deviation of the mean. Approximately 95% of the area of a normal distribution is within two standard deviation of the mean. We can perform normality tests in the Minitab to see the normal distribution. Let's see it in detail. Perform a normality test. Choose Stat Basic Statistics and then Normality Test. The test results indicate whether you should reject or fail to reject the null hypothesis that the data came from a normally distributed population. As these are the statistical terms, I will explain
2:27 them in detail in the future. At this time, only note some important things related to it. You can do a normality test and produce a normal probability plot in the same analysis. The normality test and the probability plot are usually the best tools for judging the normality. You can use the Anderson-Darling statistic to compare the fit of several distributions to determine which one is the best. However, in order to conclude one distribution is the best, its Anderson-Darling statistic must be substantially lower than the others. When the statistics are close together, you should use additional criteria such as probability plots to choose between them. 2. Squid distribution The skewed distribution is asymmetrical because a natural limit prevents outcome on one side. The
3:16 distribution peak is up-centred toward the limit and tail stretches away from it. For example, a distribution of analysis of a very pure product would be skewed because the product cannot be more than 100% pure. Other example is call handling times that cannot be less than 0. These distributions are called right or left skewed according to the direction of the tail. 3. Double-picked or bimodal distribution The bimodal distribution looks like the back of the two humped camel. The outcome of two processes with different distributions are combined in one set of data. For example, a distribution of production data from two shift operations might be bimodal. Each ship produces a different distribution of results. Statification often reveals this problem. 4.
4:06 Plateau The plateau might be called as a multi-modal distribution. Several processes with normal distributions are combined. Because there are many peaks close together, the top of the distribution resembles a plateau. 5. Edge Peak The edge peak distribution looks like the normal distribution except that it has a large peak at one tail. This is caused by faulty construction of the histogram with data lumped together into a group labeled greater than. 6. QAM Distribution In a QAM distribution, the bars are alternatively tall and short. This distribution often results from rounded off data and an incorrectly constructed histogram. For example, temperature data rounded off to the
4:52 nearest 0.2 degree would show a QAM shape if the bar width of the histogram were 0.1 degree. 7. Truncated or hard cut distribution The truncated distribution looks like a normal distribution with the tails cut off. The supplier might be producing a normal distribution of material and then relying on the inspection to separate what is within specification limit from what is out of specification. The resulting shipment to the customer from inside the specifications are the hard cut shape. 8. Dog food distribution The dog food distribution eats missing results near the average. If a customer receives this kind of distribution, someone else is receiving a hard cut, and the customer is left with a dog food. The odds and ends are left over after
5:39 the master meal. Even though what the customer receives is within specifications, the product falls into two clusters, one near the upper specification limit and one near the lower specification limit. This variation often causes problems in the customer's process. This is all about the histogram. I will explain this tool further. in the capability analysis once we started the DMeq learning in the future videos on my channel. Please like the videos on my channel if you found them meaningful and keep adding comments so that I can serve you in a better way. You can also suggest which topic you want to see in my next video. I will use that information to make this forum more interactive. Share these videos with all your friends and families who need it and subscribe my channel. Click on the bell icon after subscription to get notifications of new
6:26 videos on my channel. After clicking on the bell icon, select Get All Notifications option to get notifications of new videos added on my channel. Otherwise, you will not get it for all videos added on my channel. I will continue to share similar useful information which will help you to improve your knowledge and skills regarding continuous improvements.
