Ein Punkt des Grundstückes grafisch Datensätze mit variablen Daten in einer Weise, dass es Formen ein Bild von der kombinierten Wirkung der zufälligen Variationen aus, die in einem Prozess und den Einfluss der speziellen Ursachen auf ihn einwirken. To understand the power of dot plots as a basic tool, it helps to visualize how occurs first variant.
Sometimes explain statisticians random fluctuations by using a device called a Quincunx. A quincunx is a box with a number of pins in a way that a ball falls in arrangedAbove is randomly bounce down the pins until he comes to rest in one of the channels on the underside.
By varying the entry point of the balls, it is possible to show how to add special characters causes random variation would affect the distribution of produced values.
A funnel is used to ensure that the balls start at the same place. The pins will behave like the significant variations in other applications. After enough balls have been through the funnel, down the start filled channelsto resemble a standard distribution curve or a histogram for a uniform distribution.
For example, if the position of the moves were systematically around a central point funnel (1, 2, 3, 3, 1, 0, -1, -2, -3, -2, -1, 0, etc.) would expand the distribution of balls into the channels and shallow.
If the position would have been the funnel alternately from one side to another (5, -5, 5, -5, etc.), the balls finally show a bi-modal or two-humped distribution. This is what a mixed lotlook of the characteristics of two different suppliers, machines or workers could be produced.
If the position of the funnel is steadily moving in one direction, then again and again in the same direction (moves 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, etc.) would be the distribution of the balls form a plateau, a flat mountain with sloping sides. This could mimic what happens when a tool carries evenly, and then is replaced again and again.
If the position of moving all of the funnelnow and then in a distant, off-center position, then back to the center (0, 0, 0, 0, 0, -7, 0, 0, 0, -7, 0, 0, 0, 0, 0, 0 , 0, etc.), a group of peripheral regions, would form separate balls. This could be as random, special causes of variation, change such as voltage spikes, the process parameters.
Dot surfaces are the inverse of Quincunx experiments. To create a dot plot each data point is recorded and used as a point on a curve. As additional data points with the same value as the firstoccur, they are like the balls stacked in the Quincunx channels. After many values were recorded as points, the resulting pattern you can do something about the change in your process.
What can it do for you?
A dot plot can you form an immediate picture of the variation in your process. Often this can be a direct insight into the strategies that you could search to find the cause of these fluctuations.
Dot plots can be used during any phaseLean Six Sigma methodology. Dot plots can be found particularly useful in the measure phase.
How do you do it?
Decide which Critical-To-Quality characteristic (CTQ) You want to check. These CTQ must be measurable on a linear scale. That is, the additional value between units must be the same. For example, time, temperature, size and spatial relationships are usually measured to be in a position consistent incremental units.
Measure the characteristic andTabulation of results. If the feature is produced continuously, as in a line voltage or the temperature in a furnace, or if it produces too many items to measure all of them, you have the test. Make sure that your sample is random.
Count the number of individual data points.
Determine the highest data value and the lowest data value. Drag the lower number of the higher. This is the area. Use this offer to establish aScale. For example, if the range of your data was 6.7 points, you can use an interval of 8 or 10 for your balance.
Next, determine how many subdivisions or columns of points Your balance should be. To make an initial decision, you can chart:
Data points Subdivisions
under 50 years 5 to 7
50 to 100 6 to 10
100-250 7-12
more than 250 10 to 20
Share the selection by the numberof subdivisions. You can simplify or around that figure, it's easier to handle, but to the best picture of the distribution of data points, the number of subdivisions as possible should have been close to the top. You can set the number of measurements, if possible, raise the bid on an appropriate scale. In determining the number of parts, also consider how you measure data. Increase or decrease the number of subdivisions until it is essentially the sameNumber of ways to measure in each.
Divide the amount you elected by the appropriate number of subdivisions. Draw a horizontal line (x-axis) and label it with the scope and subdivisions.
Make a point above the scale division for each data point that falls within this sub-area. For the following data points in a subdivision, place a dot on top of the previous one. Imagine all the points the same size and keep the columns of pixels vertically.
If there SpecificationLimits for the characteristic you are studying, they are as vertical lines.
Title and label your dot-plot.
So now what?
The shape, your dot does act says a lot about your process. In a symmetrical or bell-shaped plot point is the frequency in the middle of the area is high and relatively evenly between the right and left. This form occurs most frequently. If your dot plot takes other forms, you should consult your process for otherwise unseen causes of possibleVariation.
In a comb or multimodal nature of the dot-plot, adjacent subdivisions alternate higher and lower frequency. This usually indicates a data collection problem. The problem might be to such a characteristic was measured or how values were rounded to fit in your dot-plot. It could also indicate a need to use different subdivision boundaries.
If the distribution of frequencies considerably shifted to both sides of the center of the field, the distribution will be distorted to.If a distribution is positively wrong, reduces the frequency abruptly to the left, but gently to the right. This form usually occurs when the lower limit, which is on the left side, either written or controlled, because the values do not occur below a certain value for other reasons.
If the skewness of the distribution is even more extreme, a clearly asymmetrical pit-type dot-plot is the result. This form occurs most often when a 100% screening is done fora limit.
When subdivisions are in the middle of the distribution more or less the same frequency, the resulting dot-plot looks like a plateau. This form occurs when a mixture of two distributions with different mean values blended together. Find ways, layering in order to separate the data for the two distributions. Then, you can create two separate dot surfaces reflect more accurately what is going on in this process.
If two distributions with very differentFunds are combined into one data set, divides the plateau to Twin Peaks. Here are the two distributions are much clearer than with the plateau. Also identify the examination of the data on the two different distributions will help you to understand how variation enters the process.
If there is a small, contained mainly peaks, together with a normal, symmetrical peak, this is called an isolated peak. It occurs when a small amount of data from anotherDistribution contained in the dataset. This could also include short-process anomalies a measurement error or a data collection problem.
Some final tips
A dot plot is a simple way to make a picture of the statistical variation in your process.
Dot plots can quickly give a comparative sense of records, but they have their limits. Due to the rounding of the measured data created in subdivisions, may fit the resulting shape of the dot-plotsomewhat arbitrary. A slight adjustment of the definition of subdivision can produce a somewhat different picture.
If specification limits are involved in your process, the dot plot may be a particularly valuable indicator for corrective action.
A dot plot can not only show if your process under control, but even if it is relatively centered on your target and if the change in your process within the specified tolerances.
You can not only help you see dot plots, whichProcesses will be further improved to a point of comparison at the beginning of plots with subsequent applications, they also help you track that improvement.
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