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Exploring Variations and Rules of the Mah Jongg League Card Game
Mah Jongg is a popular game that originated in China and has gained popularity all over the world. The game is played with tiles or cards, and there are various variations of the game. One popular variation is the Mah Jongg League Card Game, which uses a special card to play. In this article, we will explore the variations and rules of the Mah Jongg League Card Game.
What is a Mah Jongg League Card?
The Mah Jongg League Card is a special card that is used to play the Mah Jongg League variation of the game. The card contains various combinations of hands that players can aim for during gameplay. These combinations are divided into categories such as singles, pairs, pungs, kongs, quints, sextets, and dragons.
How to Play Mah Jongg League Card Game?
To play the Mah Jongg League Card Game, each player starts with 13 tiles or cards in their hand. The goal of the game is to create sets of tiles or cards based on specific combinations outlined on the card. Players take turns drawing and discarding tiles or cards until they have created a winning hand.
A winning hand consists of four sets and one pair or five sets without any pairs. Sets can be made up of three or four tiles or cards that match in number and suit. Pairs consist of two matching tiles or cards.
Variations of Mah Jongg League Card Game
There are various variations of the Mah Jongg League Card Game depending on which league you are playing with. Some leagues may have different combinations listed on their card while others may use different scoring systems.
One popular variation is called American Mahjong which uses slightly different rules from traditional Chinese mahjong. In American mahjong games, players draw from a wall instead of picking up discarded tiles, and jokers are used as wild cards.
Tips for Winning Mah Jongg League Card Game
To increase your chances of winning the Mah Jongg League Card Game, it is important to familiarize yourself with the combinations listed on the card. Study the different categories and try to create sets that fit into each one. Additionally, pay close attention to what your opponents are discarding as this will give you a clue as to what they may be trying to achieve with their hand.
In conclusion, the Mah Jongg League Card Game is a fun variation of Mah Jongg that is played with a special card. The game requires strategy and skill to win, but with practice and knowledge of the card’s combinations, anyone can become a skilled player.
This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.
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Every piece of data which is measured will show some degree of variation: no matter how much we try, we could never attain identical results for two different situations - each result will be different, even if the difference is slight. Variation may be defined as “the numerical value used to indicate how widely individuals in a group vary.”
In other words, variance gives us an idea of how data is distributed about an expected value or the mean. If you attain a variance of zero, it indicates that your results are identical - an uncommon condition. A high variance shows that the data points are spread out from each other—and the mean, while a smaller variation indicates that the data points are closer to the mean. Variance is always nonnegative.
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Types of Variance
Change is inevitable, even in statistics. You’ll need to know what kind of variation affects your process because the course of action you take will depend on the type of variance. There are two types of Variance: Common Cause Variation and Special Cause Variation. You’ll need to know about Common Causes Variation vs Special Causes Variation because they are two subjects that are tested on the PMP Certification and CAPM Certification exams.
Common Cause Variation
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Common Cause Variation, also referred to as “Natural Problems, “Noise,” and “Random Cause” was a term coined by Harry Alpert in 1947. Common causes of variance are the usual quantifiable and historical variations in a system that are natural. Though variance is a problem, it is an inherent part of a process—variance will eventually creep in, and it is not much you can do about it. Specific actions cannot be taken to prevent this failure from occurring. It is ongoing, consistent, and predictable.
Characteristics of common causes variation are:
- Variation predictable probabilistically
- Phenomena that are active within the system
- Variation within a historical experience base which is not regular
- Lack of significance in individual high and low values
This variation usually lies within three standard deviations from the mean where 99.73% of values are expected to be found. On a control chart, they are indicated by a few random points that are within the control limit. These kinds of variations will require management action since there can be no immediate process to rectify it. You will have to make a fundamental change to reduce the number of common causes of variation. If there are only common causes of variation on your chart, your process is said to be “statistically stable.”
When this term is applied to your chart, the chart itself becomes fairly stable. Your project will have no major changes, and you will be able to continue process execution hassle-free.
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Common Cause Variation Examples
Consider an employee who takes a little longer than usual to complete a specific task. He is given two days to do a task, and instead, he takes two and a half days; this is considered a common cause variation. His completion time would not have deviated very much from the mean since you would have had to consider the fact that he could submit it a little late.
Here’s another example: you estimate 20 minutes to get ready and ten minutes to get to work. Instead, you take five minutes extra to get ready because you had to pack lunch and 15 additional minutes to get to work because of traffic.
Other examples that relate to projects are inappropriate procedures, which can include the lack of clearly defined standard procedures, poor working conditions, measurement errors, normal wear and tear, computer response times, etc. These are all common cause variation.
Special Cause Variation, on the other hand, refers to unexpected glitches that affect a process. The term Special Cause Variation was coined by W. Edwards Deming and is also known as an “Assignable Cause.” These are variations that were not observed previously and are unusual, non-quantifiable variations.
These causes are sporadic, and they are a result of a specific change that is brought about in a process resulting in a chaotic problem. It is not usually part of your normal process and occurs out of the blue. Causes are usually related to some defect in the system or method. However, this failure can be corrected by making changes to affected methods, components, or processes.
Characteristics of special cause variation are:
- New and unanticipated or previously neglected episode within the system
- This kind of variation is usually unpredictable and even problematic
- The variation has never happened before and is thus outside the historical experience base
On a control chart, the points lie beyond the preferred control limit or even as random points within the control limit. Once identified on a chart, this type of problem needs to be found and addressed immediately you can help prevent it from recurring.
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Special Cause Variation Example
Let’s say you are driving to work, and you estimate arrival in 10 minutes every day. One day, it took you 20 minutes to arrive at work because you were caught in the traffic from an accident zone and were held up.
Examples relating to project management are if machine malfunctions, computer crashes, there is a power cut, etc. These kinds of random things that can happen during a project are examples of special cause variation.
One way to evaluate a project’s health is to track the difference between the original project plan and what is happening. The use of control charts helps to differentiate between the common cause variation and the special cause variation, making the process of making changes and amends easier.
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Identifying and Managing Special Cause Variations
Updated: June 14, 2023 by Lori Kinney
Common cause variations are those variations that happen as part of natural patterns. These kinds of variances are inherent in the process , consistent, and normal. Special cause variations are quite different than these sorts of variances.
Overview: What is Variation (Special Cause)?
These are the variances that are unexpected, non-quantifiable, and unusual. Special cause variations are those that in a process have not been encountered before. They are caused by unpredictable factors. Examples of special cause variations include machine faults, power surges, operator absences, and computer faults.
3 drawbacks of special cause variations
There are some drawbacks to special cause variations that should be acknowledged:
1. They can be difficult to prepare for
Special cause variances can be so random that it can be extremely difficult to adequately prepare for them.
2. They may cause serious problems
Some special cause variations can be eliminated due to them being a technical issue. If preventable, they should be as they can cause major problems for the business.
3. The source can be difficult to find on a control chart
The source of special cause variations can be difficult to spot on a control chart if you are not plotting in real-time. To find the source of the special cause, you would likely have to have annotated data or an exceptionally good memory.
Why are special cause variations important to understand?
For the following reasons, special case variations are important to understand:
If you have a chart that only has common cause variations, it means that your process is likely “statistically stable.” Understanding special cause variations on your chart, helps you recognize the inverse. It means that your processes are “statistically unstable” and that modifications may need to occur.
You can spot them on a control chart
By understanding special cause variations, you can probably pretty easily spot them on a control chart. Should the measurements of a process be distributed normally, it is almost guaranteed that a measurement will fall within plus/minus three deviations. Measurements that fall outside these limits are likely to be special cause variations.
Understanding them makes them easier to investigate
If you get a measurement on a control chart that appears to be a special cause variation, the expectation is that it will be investigated, there will be a root cause analysis, and appropriate measures will be taken.
An industry example of a special cause variation
A project manager has been leading the test-drilling for a new site where it is believed that there is likely a significant amount of untapped crude oil. The test-drilling was supposed to last for a week, but one of the drills malfunctioned, which caused a delay. Instead, the test-drilling took a total of 30 days once the faulty drill issue was addressed. The malfunction is an example of a special cause variation.
3 best practices when thinking about special cause variations
Here are a few practices to bear in mind when it comes to special cause variations:
1. Countering special cause variations
Contingency plans can be used to counter special cause variations. With this strategy, additional processes are incorporated into operations that prevent or counter a special cause variation.
2. Recognize them on a control chart
Remember that if it falls plus/minus three deviation limits of a measurement on a control chart, it is probably a special cause variation.
3. You can avoid over-tampering
If you do not understand how variation works, you could feasibly over-adjust, which can then lead to even more variation every time there is a process change.
Frequently Asked Questions (FAQ) about Variation (Special Cause)
What is assignable cause.
It is another term for special cause variation.
Who introduced the idea of special cause variations?
M. Edwards Deming was an engineer and statistician who is considered a founding father of Total Quality Management. He is credited with being the person that came up with the concept of special cause variation.
What percentage of issues relate to special cause variations?
Special cause variations account for less than 10%. Common cause accounts for closer to 90% of variances that may occur.
Eliminating special cause variations
While it can be difficult to predict an initial special cause variation, steps can be taken to help ensure that the same problem does not arise again. Technical improvements, proper training, and other strategies can all help protect your company from repeat incidents.
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What is the variation?
Whatever measurement we take, there is always a variation between these measurements. No two items or measurements are precisely the same.
The problem with the variation is that it is the enemy of quality. Variation and quality do not go hand in hand. Variation reduction is one of the significant challenges of quality professionals.
Two types of variation, and why is it important to differentiate?
When dealing with variation, the challenge quality professionals face when to act and when not to act. Because if you act on each and every variation in the process and adjust the process, this will be a never-ending process. Dr. Deming called this "tempering the process." Rather than improving the quality, tempering, in fact, reduces the quality. Deming demonstrated the effect of tempering with the help of a funnel experiment.
The causes of variation can be classified into two categories:
- Common Causes
- Special Causes
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Common Cause Vs Special Cause: Types of Variation
Common cause variation is the natural variation in the process. It is a part of the process. There are "many" causes of this type of variation, and it is not easy to identify and remove these. You will need to live with them unless drastic action is taken, such as process re-engineering.
Common causes are also called n atural causes, noise, non-assignable and random causes .
Special cause variation, on the other hand, is the unexpected variation in the process. There is a specific cause that can be assigned to the variation. For that reason, this is also called as the assignable cause . You are required to take action to address these variations.
Special causes are also called assignable causes .
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Control Charts to identify special causes
If the measurements of a process are normally distributed, then there is a 99.73% chance that the measurement will be within plus and minus three standard deviations. This is the basis of control charts.
If you plot the measurements on a Control Chart, then any measurements which are outside the plus and minus three standard deviation limits are expected to be because of a special cause. These limits are called as the Upper Control Limit (UCL) and the Lower Control Limits (LCL), Once you get such measurement, you are expected to investigate, do the root cause analysis , find out the reason for such deviation and take necessary actions.
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Using control charts to detect common-cause variation and special-cause variation
In this topic, what are common-cause variation and special-cause variation, what special-cause variation looks like on a control chart, using brainstorming to investigate special-cause variation, don't overcorrect your process for common-cause variation.
Some degree of variation will naturally occur in any process. Common-cause variation is the natural or expected variation in a process. Special-cause variation is unexpected variation that results from unusual occurrences. It is important to identify and try to eliminate special-cause variation. Out-of-control points and nonrandom patterns on a control chart indicate the presence of special-cause variation.
Examples of common-cause and special-cause variation
A process is stable if it does not contain any special-cause variation; only common-cause variation is present. Control charts and run charts provide good illustrations of process stability or instability. A process must be stable before its capability is assessed or improvements are initiated.
This process is stable because the data appear to be distributed randomly and do not violate any of the 8 control chart tests.
This process is not stable; several of the control chart tests are violated.
A good starting point in investigating special-cause variation is to gather several process experts together. Using the control chart, encourage the process operators, the process engineers, and the quality testers to brainstorm why particular samples were out of control. Depending on your process, you may also want to include the suppliers in this meeting.
- Which samples were out of control?
- Which tests for special causes did the samples fail?
- What does each failed test mean?
- What are all the possible reasons for the failed test?
A common method for brainstorming is to ask questions about why a particular failure occurred to determine the root cause (the 5 why method). You could also use a cause-and-effect diagram (also called fishbone diagram).
While it's important to avoid special-cause variation, trying to eliminate common-cause variation can make matters worse. Consider a bread baking process. Slight drifts in temperature that are caused by the oven's thermostat are part of the natural common-cause variation for the process. If you try to reduce this natural process variation by manually adjusting the temperature setting up and down, you will probably increase variability rather than decrease it. This is called overcorrection.
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Common cause and special cause variation
Published on 13 April 2023 at 16:39
by Suzie Creighton
In past articles in this series of blogs, we have looked at Statistical Process Control (SPC) and how it works within the Quality Improvement (QI) setting. We know that SPC charts are designed to measure how a process or system changes over time.
We’re going to look in a bit more detail about how SPC charts identify the difference between positive or negative variation as well as normal and unusual variation in the data. We’re also going to show you the specific types of variation that can exist within an SPC chart, namely: common cause and special cause variation.
Just to reiterate, SPC charts – also known as control charts – are ‘decision-making tools that provide information for timely decisions concerning recently produced products’ . They enable you to monitor and review the progress of QI projects and to look at trends or variation.
SPC charts have an average or mean line, and two control lines which sit above and below the average line. This enables a greater depth of statistical interpretation.
SPC charts also show individual data points, which allows more granularity. You can also use them alongside PDSA cycles – which you may well be familiar with from your QI work.
The Institute for Healthcare Improvement (IHI) recommends the visual display of data as the best way to learn from variation in QI, saying: ‘For improvement efforts, visual displays of data are often the best approach to learn from variation in data. Images are usually easy and quick to prepare, and they make it possible to access nearly all kinds of potential insight from the data.’
Let’s take a look at the variations we can monitor in our SPC charts: common cause and special cause variation.
What is a common cause variation?
In Quality Improvement, teams consistently use data. Both to learn from the processes they are carrying out and to predict future performance. 'Quality improvement requires using data to learn and to predict future performance. In improvement, it is critical to understand that every process has inherent variation that we want to understand.'
When you are looking at your SPC chart, you should be on the look out for two types of variation: common cause and special cause variation.
Common cause variation is something that is to be expected within your project. ‘Variation that is expected and natural in the system. Whilst this does not indicate that the system is working well, it does mean that the system is stable and predictable’. Common cause variation will always be present and is intrinsic to the whole process.
Here’s how the NHS Institute for Innovation and Improvement's document 'Guide to creating and interpreting run and control charts’ describes the common cause variation.
Common cause variation:
- ’is inherent in the design of the process
- is due to regular, natural or ordinary causes
- affects all the outcomes of a process
- results in a “stable” process that is predictable
- also known as random or unassignable variation’
What is a special cause variation?
Special cause variation stems from external sources and control. This type of variation is not expected and therefore not a consistent part of the process. Special cause variation demonstrates that the process is out of statistical control. They show that while ‘the causes can be often infrequent, they can result in a chaotic problem. The cause could possibly be a defect in the system or a problem that has never happened before.’
Special cause variation:
- is due to irregular or unnatural causes that are not inherent in the design of the process
- affect some, but not necessarily all aspects of the process
- results in an “unstable” process that is not predictable
- also known as non-random or assignable variation’
When you note a special cause, you shouldn't implement PDSA cycles, as it shows that the system is not stable.
How to react when you see variation?
We’ve taken a look at common and special cause variation, but you may well be wondering what you should do when you encounter either type of variation within your QI project? Let’s take a look.
NHS England and Improvement's SPC document explains that it is normal for a process to demonstrate no cause of special variation, and therefore recommends that when you identify common cause variation, you should change the underlying process itself rather than reacting to individual performance changes. This would lead to more variation. By reducing variation you can improve your processes.
The NHS Institute for Innovation and Improvement recommends that if your process is demonstrating special cause variation, you should look into the origin of the cause. They add that: ‘changing the underlying process on the basis of special causes is a waste of resources.’
The East London NHS Foundation Trust (ELFT) use Life QI and have an excellent guide to interpreting SPC charts . The team recommends taking the following steps when you note special cause variation:
- Investigate what caused it? Was it due to internal (i.e. change in process) or external factors (i.e. cyber attack)?
- Determine is any action is needed
- Revise control limits (calculate new CL, UCL and LCL) if appropriate’
Rules in detail
Now let’s take a look at rules pertaining to special and common cause variation, and explore the terms you might well have heard in your QI journey.
What is a shift?
This can take place in a SPC or control chart: ‘a shift in its most basic form is eight points in a row either above or below the centerline.’
- A Shift is a run of eight or more data points in a row above or below the centre line
- Low variation is fifteen consecutive points in the inner third of the chart, between the -1 and +1 sigma limits.’
What is a trend?
A trend is six points in a row in an upward direction or in a downward direction.
‘The use of a shift or a trend is that when seen, it is a ‘smoking gun’ for an area of improvement. The team is really hoping to see a trend or a shift because if a process always stays in the state of statistical control, the opportunity of reducing assignable-cause variation is far more challenging then if assignable-cause variation is evident.’
What is high variation?
This is when two out of three points in the outer third of the chart, between the +2 and +3 sigma or between the -2 and -3 sigma limits.
What is an outlier?
An outlier occurs when there is a single data point above the upper control limit (UCL) or below the lower control limit (LCL).
In this article we’ve had a really good look at variation in SPC charts. As we have seen before, if you are using a software solution such as Life QI, this can make life easier for you and your QI team to interpret and act on your SPC charts to deliver better results.
To finish with the words of NHS England and Improvement's SPC document . ‘There are four rules to interpret SPC charts and if you use specialist software, these rules will be flagged so you don’t need to remember them. If one of the rules has been broken, this means that special cause variation exists in the system and once identified, can be removed.’
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Encyclopedia of Production and Manufacturing Management pp 717 Cite as
SPECIAL CAUSES OF VARIATIONS
- Reference work entry
W Edwards Deming elaborated on Walter A. Shewhart's argument that variability in manufacturing and service processes can be traced to either common causes or special causes of variations (Shewhart's assignable causes). Special causes variability is beyond the natural variability of the process. Special cause variability can be identified and addressed by operators. Examples of special causes are operator error, faulty setup, or incoming defective raw material. Deming believed that only about 15% of the variation in a process is due to special causes. Deming relied on control charts to describe both the natural variability of the system, and to detect the existence of a special causes of variation. A process that is operating with special causes of variation is said to be “out of statistical control.”
See Quality: The implications of W. Edwards Deming's approach ; Statistical process control using control charts ; Total quality management .
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Deming, W. Edwards (1982). Out of the Crisis, Center for Advanced Engineering Study, Massachusetts.
Deming, W. Edwards (1982). Quality, Productivity, and Competitive Position, Center for Advanced Engineering Study, Massachusetts Institute of Technology, Cambridge, Massachusetts.
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(2000). SPECIAL CAUSES OF VARIATIONS . In: Swamidass, P.M. (eds) Encyclopedia of Production and Manufacturing Management. Springer, Boston, MA . https://doi.org/10.1007/1-4020-0612-8_905
DOI : https://doi.org/10.1007/1-4020-0612-8_905
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