DOWNLOAD PDF MANUAL ON PRESENTATION OF DATA AND CONTROL CHART ANALYSIS

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1 Chapter 1 : ASTM manual on presentation of data and control chart analysis ( edition) Open Library tis Atmh s Manual on Presentation of Data and Control Chart Analysis is the ninth edition of the Astm Manual on Presentation of Data first published in this revision was prepared by the Astm E subcommittee on statistical Quality. Data are plotted in time order. A control chart always has a central line for the average, an upper line for the upper control limit and a lower line for the lower control limit. These lines are determined from historical data. By comparing current data to these lines, you can draw conclusions about whether the process variation is consistent in control or is unpredictable out of control, affected by special causes of variation. Control charts for variable data are used in pairs. The top chart monitors the average, or the centering of the distribution of data from the process. The bottom chart monitors the range, or the width of the distribution. If your data were shots in target practice, the average is where the shots are clustering, and the range is how tightly they are clustered. Control charts for attribute data are used singly. When to Use a Control Chart When controlling ongoing processes by finding and correcting problems as they occur. When predicting the expected range of outcomes from a process. When determining whether a process is stable in statistical control. When determining whether your quality improvement project should aim to prevent specific problems or to make fundamental changes to the process. Template See a sample control chart and create your own with the control chart template Excel, KB. Determine the appropriate time period for collecting and plotting data. Collect data, construct your chart and analyze the data. When one is identified, mark it on the chart and investigate the cause. Document how you investigated, what you learned, the cause and how it was corrected. Out-of-control signals A single point outside the control limits. In Figure 1, point sixteen is above the UCL upper control limit. In Figure 1, point 4 sends that signal. In Figure 1, point 11 sends that signal. A run of eight in a row are on the same side of the centerline. Or 10 out of 11, 12 out of 14 or 16 out of In Figure 1, point 21 is eighth in a row above the centerline. Obvious consistent or persistent patterns that suggest something unusual about your data and your process. Figure 1 Control Chart: Out-of-Control Signals Continue to plot data as they are generated. As each new data point is plotted, check for new out-of-control signals. When you start a new control chart, the process may be out of control. If so, the control limits calculated from the first 20 points are conditional limits. When you have at least 20 sequential points from a period when the process is operating in control, recalculate control limits. Excerpted from Nancy R. Page 1

2 Chapter 2 : Statistical Process Control (SPC): Basics and free training ASTM Manual on Presentation of Data and Control Chart Analysis STP 15D by American Society for Testing and Materials and a great selection of similar Used, New and Collectible Books available now at theinnatdunvilla.com Legend Trendline In Excel, categories are plotted on the horizontal axis and data series are plotted on the vertical axis: From the chart above, we can conclude the following: Months are plotted on the primary horizontal axis. Sales, cost and profit are plotted on the primary vertical axis. ROI is plotted on the secondary vertical axis. In order to add, remove or edit a chart element in Excel, follow the steps below: Open MS Excel and navigate to the spreadsheet which contains the chart you want to edit. Click on the chart in Excel Step You can now see your chart with data table: Visualise data make sense of data esp. Nominal data â qualitative data that can not be put into a meaningful order i. In other words, avoid using column charts if you have just one data series to plot: Alternatively, avoid creating a column chart which has got more than four data series. For example following chart contains just five data series and it has already started looking cluttered: For example, you can create one column chart which just compare the sales performance of various countries in January. The rule of thumb is to avoid presenting too much data in one chart, regardless of the chart type you use. When to use combination charts A combination chart is simply a combination of two or more charts. For example the combination of a column chart with a line chart. I use combination charts a lot and I think you must know how to create them as they are very useful. Following is a short video on creating a combination chart in excel: When to use a number chart? The more data points the better it is for a scatter chart. Conversely just a few data points like five or six data points are not good enough for creating a scatter chart. What that means, do not create a pie chart where the various pie slices do not represent parts of the whole pie. For example following pie chart shows the breakdown of a website traffic sources in the last one month: Here I have got only four categories search traffic, referral traffic, direct traffic and campaigns to plot. So pie chart is ideal to show the breakdown. However if there were more than four categories to plot, like eight or ten categories, then the pie chart would have become cluttered and hard to read. For example following pie chart looks cluttered because it has got too many categories: This makes the pie chart easy to read: When to use a stacked area chart Use a stacked area chart when you want to show the trend of composition and emphasise the magnitude of change over time. For example following stacked area chart shows the breakdown of website traffic: When to use a histogram Use histogram to show frequency distribution for quantitative data: When to use a Venn diagram Use a Venn diagram to show overlapping of data. The multi-channel conversion visualizer chart used in Google Analytics to visualize multi-channel attribution is actually a Venn diagram: In the context of web analytics, we can use a Venn diagram to determine whether or not a website has got attribution problem. If there is a good amount of overlap then the website has got attribution issues and you should seriously consider taking multi-channel attribution into account while analyzing and interpreting the performance of marketing campaigns. To learn more about attribution modelling read this article: Beginners Guide to Google Analytics Attribution Modelling Another great use of Venn diagrams is in visualizing the back links overlaps between websites: The tool that I have used to create this Venn diagram is known as Venny. You can create a Venn diagram in Excel. Check out this tutorial on Microsoft Office website: Following are those charts: Page 2

3 Chapter 3 : Manual 7 MNLTH Presentation of Data and Control Chart Analysis â 9th Edition ASTM MNL7: Manual on Presentation of Data and Control Chart Analysis Manual on Presentation of Data and Control Chart Analysis. View Abstract. We see that the chart is able to detect both disturbances in the average as well as disturbances in the range. The chart shows some instability, both by having some points outside the control limits and because there are long runs in the data. A run is where a number of consecutive results are all above average or all below average. Lets look at how control limits for individual value chart are calculated: During an implementation we will also implement control charts where removing instability is not the highest priority because it is not the most critical characteristic. In that case we may use different ways to calculate limits. This advanced subject is outside the scope of this training. Shewhart control charts will indicate instability even if instability is present in the data used to calculate the control limits. We must use our knowledge of the process when deciding how to sample results and arrange them into subgroups. We should do this in a way which we know will reduce the chances of special cause variation occurring within subgroups. With these charts, the control limits are based on the average difference between each individual result. If we want to detect if the process is stable it is a mistake to calculate the control limits from the deviation of the individual results from the Average. The distance of the control limits from Average is calculated from a short-term dispersion statistic subgroup range or moving range. In lesson 4 the X individual value chart was introduced. In both these cases, we used variable or measurement data. This is data which comes from a continuous scale. Attribute data comes from discrete counts. If we know in advance that the set of data will exhibit the characteristics of Binomial data or Poisson data then these types of charts should be used. Binomial data is where individual items are inspected and each item either possesses the attribute in question or it does not. Each bead scooped is either blue or it is not blue â so if we create a stream of samples taken from the box and we count the number of blue beads in the samples, then we can assume that the resulting data will be Binomial type data. Other examples of counts which would generate binomial data are: Late deliveries Non-conforming goods Out of specification components. The random variation of Binomial data acts in a particular way, because of this we can calculate where to put the control limits. All we need to know is the average of the data set and the sample size. It is used when we know we have Binomial data and the sample size does not change. Binomial data with different sample sizes: If we have binomial data but the sample size is not constant, then we cannot use a np chart. We will now use the simulation to add new samples to the data we have already started, but we will change the sample size: When the sample size is not constant for every scoop we have to convert counts to a rate or proportion. We convert to a rate by dividing the attribute count by the sample size. You will notice that there is a step in the control limit lines at the point where the sample size changed. The purpose of the control limits is to show the maximum and minimum values that we can put down to random common cause variation. Any points outside the limits indicate that something else has probably occurred to cause the result to be further from the average. As we have said before, the random common cause variation of Binomial data acts in a particular way. The variation with large sample sizes is smaller than the variation with small sample sizes. We can use the simulation to demonstrate this. We will change the subgroupsize to 5 and take 30 more subgroups Look at the results in the Data Table and keep in mind that the proportion of red beads in the box has not changed. In rare cases like in this simulation we can even have 4 and we have a false alarm. Look again at the results in the Data Table. Look at the way the points which correspond to the small sample size samples 60 â 90 vary up and down, then compare this with the variation with the large sample size after Keep in mind that we are not looking at absolute numbers here, we are looking the proportion of the sample which is red. Look at the position of the control limits for the small subgroupsize and the large subgroupsize. This illustrates one of the basic points about using control charts for attributes. Small subgroupsizes produce control charts which are not sensitive because there is so much random common cause variation in small Page 3

4 sample sizes. Large sample sizes produce more sensitive control charts. What this means is that if a process has a special cause of variation acting on it from time to time, it may not produce any points outside the control limits if the sample size is small. The same special cause of variation is more likely to produce points outside the control limits if we use a large sample size. Notice that the limits have to be separately calculated for each subgroupsize. The example given is for sample number 1 subgroups 1 to Criteria for binomial data: We can only use an np chart or a p chart if we know in advance that the data produced will be binomial data. The full conditions which have to be satisfied before we can consider a set of data to be Binomial are: The count must arise from a known number of discrete products goods or services. Each product inspected must either have, or not have, the attribute which we are counting. The products inspected must not influence each another. If one item has the attribute, this fact must not change the likelihood of its neighbours having the attribute. Data from process can be divided into two major categories, variables and attributes. Binomial data is attribute data where individual items are inspected and each item either possesses the attribute in question or it does not. With applying SPC to attribute counts, small sample sizes make it difficult to distinguish between common cause variation and special cause variation. For example, we might want to count the number of blemishes on a surface. Criteria for Poisson data: We can consider data to be Poisson type data if: Discrete counts of an attribute can be made. The counts arise from a known area of opportunity. As with Binomial data, the attributes must arise independently of one another. In other words, there must be no mechanism which makes the attribute normally occur in clusters. There are relatively few incidents of the attribute appearing compared with what might happen in the worst possible circumstances. The only difference is the way the control limits are calculated: Notice that the sample size is not used anywhere in these calculations. The rate is simply the attribute count divided by the sample size or area of opportunity for the sample. Look at how the limits are calculated Notice that the control limits are tighter for larger areas of opportunity. The X individual value control chart with attribute data: In a lot of cases the Binomial or Poisson charts are not appropriate because one of the conditions is not applicable. Control limits for X charts are empirical limits based on the variation in the data and these are almost always valid. First we will generate some data: Now we will create a Binomial chart and an X individual values chart from same data. If we cannot be confident that the data we have fulfills the conditions to be binomial or Poisson data, then we can usually rely on an X chart to do a pretty good job. However there are limitations: We now have a non constant sample size. Sometimes X charts should be rate charts when the sample size is not constant and sometimes they should not â it depends on what the measurement represents. In our case the number of red beads scooped is definitely dependent on the sample size so we should look at an X chart based on rates. The p chart and the X rate chart are both showing proportions and the control limits have been calculated using scoops 1 â Compare the two charts. Look at the data and the control limits before and after the change of sample size the change was at subgroup number Because we have not changed the number of beads in the box, we are looking at the results of a stable process so in theory control charts should not show any points outside the control limits. There is always more random common cause variation with small sample sizes and you can see that the points on both charts jump up and down more after we change to a smaller sample size. Because the control limits on a binomial chart are based on a theoretical knowledge of the way binomial data behave, the control limits change to accommodate the different sample sizes. On X charts, the control limits are based on the variation between successive points in the data stream. When this variation changes due to altering the sample size, this can be misinterpreted as a process change. X charts with low average: When the average count is very small, another problem prevents us from using X charts. With attribute counts, the data can only take integer values such as 6, 12, 8 etc. Values such as 1. The discreteness of the values is not a problem when the average is large, but when the average is small less than 1 then the only values which are likely to appear are 0, 1, 2 and occasionally 3. The whole idea of control charts is that we want to gain insight into the physical variations which are happening in a process by looking at the variation of some measurement at the output of the process. When the measurements are constrained to a few discrete values then the results are not likely to reflect subtle physical changes within the Page 4

5 process. For this reason X charts should not be used for attribute counts when the average count is low. Lesson 9 gives more information about using attribute control charts when the average count is low. If we cannot be sure that the data will meet all the conditions to be Binomial or Poisson data, then we may be able to use an X chart, but the average count must be greater than 1. Chapter 4 : Control Chart - Statistical Process Control Charts ASQ Manual on Presentation of Data and Control Chart Analysis (8th Edition): (MNL th) Details This book is a valuable reference for anyone responsible for controlling product quality. Chapter 5 : Best Excel Charts Types for Data Analysis, Presentation and Reporting ASTM Manual on Presentation of Data and Control Chart Analysis - STP 15D 1 edition By American Society for Testing and Materials. ASTM Manual on Presentation of Data and Control Chart Analysis - STP 1. Chapter 6 : ASTM Manual on Presentation of Data and Control Chart Analysis - STP 15D Open Library Part 3 covers the control chart method for the analysis of observational data obtained from a series of samples, and for detecting lack of statistical control of quality. Part 4 discusses material on measurement systems analysis, process capability, and process performance. Chapter 7 : Manual on Presentation of Data and Control Chart Analysis (8th Edition): (MNL th) - Knovel analysis is a bigger waste than not collecting data in presentation of masses of numerical data statistical methods (Control charting, Regression Also, per the AIAG PPAP manual (4th Edition) if the above. Chapter 8 : ASTM manual on presentation of data and control chart analysis in SearchWorks catalog Part 3 covers the control chart method for the analysis of observational data obtained from a series of samples, and for detecting lack of statistical control of quality. New Part 4 discusses material on measurement systems analysis, process capability, and process performance. Chapter 9 : Product News: Presentation of Data and Control Chart Analysis Manual, New Edition Quality D The user data structures algorithm analysis solution manual could possibly ASTM MANUAL ON PRESENTATION OF DATA AND CONTROL CHART. The computer program controls the cytometer during data acquisition. Page 5