1/8/2024 0 Comments Xbar chart minitab![]() The last subgroup is highlighted and on the right side we see that Xbar = 533.2 and the Range = 69. For each row in the data table, the subgroup average is plotted (Xbar) along with the largest value in the subgroup minus the smallest value (Range). Now let’s look at the control chart of this data:Īn Xbar and range chart contains two graphs. The subgroup columns contain the results of shots 121, 122, 123, 124 and 125. Look at the row with the number 25 in the grey column at the left. For each row of the table, the data in these five columns represents one subgroup. The number in brackets means that the column is part of a subgroup. Notice that there are 5 columns with “Landing position ( )” at the top. Let’s take a quick look at the data table. In an Xbar and Range chart, the data is arranged into subgroups. We will use an Xbar and Range chart as the control chart for this process. This type of data is called variable data. We are actually using complete numbers for the results (423, 657 etc.) but there is nothing to stop us using more accurate measurements if we wanted to (423.45, 657.09 etc.) The landing position is a continuous scale. We fire off another 100 shots then we will create a control chart. So, we need to find out if the process is “statistically stable”. At this stage we do not know very much about our process and we do not know if things are likely to change over time. “Assuming that nothing changes” is a big assumption. Assuming that nothing changes in this process, future output should now be centered on 500. We can now move the launcher so future shots will center around 500. After 50 shots the process average is 419 (Rounded) If we fire 45 more shots we will get a better estimate of the real average of the process. We can calculate the average of the shots which is 413 (Rounded). ![]() We now have more information to get an idea how the process is performing. Each landing position is different and the variation may be due to common causes which are always present in this process. Look at the display of Landing positions at the right of the launcher. There is always a certain amount of variation in every process and if we only have common cause variation and we are trying to adjust for this variation we will actually cause more variation on the output. The answer is NO because we don’t know the process yet. The customer of this process wants the landing position to be 500, so what do we do now? Should we compensate for the error by moving the launcher? When we fire one ball from the launcher we find the value is 415. Your job is to fire balls at the target and get them to land as close as possible to the ideal value of 500. Imagine that you are the operator of a machine – the launcher. The location of the previous shots fired are given in the screen. ![]() The target is 500 and the specifications are 300 to 700. The process is a tennis ball launcher and we are trying to shoot balls at a target distance. Our job is to try to get results as close as possible to that target. In this lesson we are going to look at a process where we have been given a preferred target value for a variable measurement. Attribute data, on the other hand, can only have whole number values like 1, 3, 12 etc. With variable data we can measure to any accuracy that we want, for example 12.5, 3.075 etc. The number of blemishes on a surface, the number of faulty products and the number of unpaid invoices. Length, weight, temperature and pressure.Īttribute data is based on discrete counts. Variable data is any measurement which has a continuous scale. Data can be divided into two major categories, variables and attributes. We use different types of control charts for different types of data. Now we are going to learn how to draw the control charts. In lesson 1 we discovered why we need control charts.
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