R Chart Minitab

[Dallas Querry]

Evermake a task harder than it needs to be? Last night my son wanted to get a toy from a shelf in the closet that was a little beyond his reach. I watched as he tried to jump up and grab it. Next, he started climbing the shelves (a quick word from me stopped that approach). Then he tried jumping several more times. Finally he said "I give up."

Statistics can be like that. We can focus on the complexities of an analysis, but lose sight of the practical question we're trying to answer. If we step back and look at all of the available tools, sometimes there's a simpler way to get the information we need.

When seeking to improve quality, you often look for processes with unusual variation so you can identify what's causing the variation, then control it. A process is in control when only natural variation affects the output. That's the idea behind control charts, and Minitab makes it easy to create a wide variety of control charts depending on what type of data you're looking at.

But step back a second. Some variation is a natural part of any process, and in the quality world we're concerned only about that unusual, "special cause" variation. You can use a simple tool -- the run chart -- to see if special causes are influencing your process. A run chart can't tell you everything that a control chart can, but if a run chart suggests a process is already under control, perhaps you can find a process in more urgent need of improvement!

Minitab's run chart plots individual observations in the order they were collected, and draws a horizontal reference line at the median. Minitab also performs two tests that provide information on non-random variation due to trends, oscillation, mixtures, and clustering -- patterns that suggest the variation observed is due to special causes.

It's very easy to make a run chart in Minitab. Let's say you work for a company that makes radon detectors. To make sure that detectors using a certain kind of membrane measure the amount of radiation consistently, you test 20 devices (in groups of 2) in an experimental chamber. You record the amount of radiation each device measured after every test.

The black points on the run chart represent the individual values, while the red points connected with a line represent the subgroup means. Visually, it looks like there's some unusual variation happening for tests 3, 4, and 5. This is confirmed in the data for the normality tests: the p-values for mixtures, trends, and oscillation look fine, but the test for clustering is significant at the 0.05 level.

Because the p-value for the cluster test (p = 0.022) is less than the alpha value of 0.05, you can conclude that special causes are affecting your process, and you should investigate possible sources. Clusters can be evidence of sampling or measurement problems, but you'll need to collect more data to be sure.

In this case, the run chart detected unusual variation, so you'll need to dig deeper to find out what's going on with your data. But if the chart hadn't shown variation, you could be more confident about the accuracy of your membrane detectors and dedicate your time to improving a process that might be more problematic.

In the example above, raw data were used. In other words, the data file contained one row for each case. It is also possible to use Minitab to construct a pie chart with summarized data, for example, if you have your counts in a frequency table. If this is the case, follow the steps below. This example uses the following data concerning Penn State undergraduate enrollment:

P Charts are Control Charts designed for tracking the proportion defective for discrete data.These charts require both the total population as well as the count of defective units in order to plot the proportion.

Since MiniTab can now take external data and generate graphs and charts from it, I thought it may be beneficial to pass this along to others that may want this functionality in one of their apps. The BD is wide and not optimized for speed but the code does work. By all means go ahead and make this your own and speed it up where you think you can. I am still working on what Commands need to be fed into the Project Node.

By no means am I an expert as Labview coding or the MiniTab API. I'll help where I can but I may not get to in a timely manner. My company was working on a project using Excel data to MiniTab and returning reports back and thought I would try Labview and it works.

As to the MiniTab report part...how I did it was I loaded the data I was going to use directly into MiniTab and just started played with what graph or chart I wanted until I got the desired output that I was looking for. Then I went into Show Histroy (CTRL+ALT+H) and looked at the code used to generate that Minitab chart/graph. Copy and Paste that into the ExecuteCommand "Command" Invoke Node (it expects a string) and you have a photo of your chart to do whatever you want to do with it. Remember, this is version 17 so newer versions could be different.

I too need to create graphs for the calculation of Cp and Cpk and I would like to automate the procedure. What have to be installed on the pc regarding Labview and Minitab?......Are there any particular libraries?

I would like to use the features of minitab to create the graphs....not only Cp and Cpk but also histograms, time series graphs, etc.

I saw the example you shared, how did you create the reference to minitab?

I have installed Labview 2017 and minitab18

How I did it was I placed a Application Refrum right on the Front Panel, then right-clicked, choose the Select ActiveX Class, then searched for, I think, it's MTB Type 17.0. Yours might be MTB Type 18.0. Then wire it up using the Automation Open ActiveX icon.

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One of the most useful charts to visually represent where areas of concern in a business may be is the Pareto Chart. The chart identifies the Pareto principle, or what many call the "law of the vital few," or more often, the "80:20 rule." The principle suggests that most effects come from a small amount of causes. By creating the Pareto chart, areas of concern are easily identifiable.

Below are step-by-step instructions on how to run a Pareto chart in Minitab. The data used in the following example can be downloaded in .MTW format Pareto Chart.MTW. It shows the count of defects across five different teams.

The purpose of running a Pareto analysis on this data set was to find how many defective products were being created by each team. In this example, your effects are the defects, and the causes are the teams. The chart created by Minitab has sorted the teams in descending order by the number of defects created. Team 4 was placed on the left with 25 of the 50 total defects. The percent under the count of defects shows what percentage of the total defects the team was accountable for. The connected data points above the bars represent cumulatively what each team contributes as a percentage to the total number of defects. The graph shows that about 80% of the effects come from 20% of the causes.

Now that the graph has been interpreted, the next step would be to continue analyzing the data to identify what is causing the effects in the 20%. A second and third-level Pareto chart should be created to identify the root cause for defects among the team.

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I have a Minitab Project with two Worksheets. They are both linked two different cells in a single Excel sheet which update at specific intervals. So, each time the values in the Excel cells update they will be added onto the next row in the corresponding Minitab sheet.

I did previously try to do the job without any code in Minitab. Just a link to the cells in Excel with a chart set to auto-update. As above this works fine with just one worksheet, but involving another sheet means that only the currently active sheet's chart will update.

Now I've taken out the auto-updating chart. I have included the below code which will create a new chart every time there is a new value added. I didn't originally want to do this but I've figured out how to make it close all existing charts.

Minitab provides a great Gage R&R Sixpack (6 sections) report, when performing a measurement systems analysis (MSA) study. However, there is some confusion and a lack of knowledge on how to interpret each chart, in order to better understand the validity of your measurement system.

% Contribution is the percentage of overall variation from each variance component: Repeatability, Reproducibility (Operator and Operator*Samples) and Part-to-Part variation. These percentages are related closely to the % for Repeatability and Reproducibility in other tables, but they sum up to 100% (where the other ones do not sum to 100%, which is confusing for many). The criteria is to have less than 1% of the variation due to Total Gage R&R, and no more than 9%. Anything between 1 and 9% would be considered marginal.

Here is the criteria for determining if your measurement system is adequate, using the different % calculations in Minitab (derived and influenced from AIAG guidelines for the gage R&R table, but not the exact same conclusions).

Range Chart by Operator

The next chart, the R chart (middle left), shows the repeatability and reproducibility variation. If your R chart is in control (almost all data points inside the control limits), then that is a good sign. If it is out of control (points outside control limits), then there is no consistency to the measurement system.

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