Enter the data into a blank Minitab worksheet with one column containing the Campus names and a second column containing the Count for each campus.To create a pie chart using summarized data: Penn State Fall 2019 Undergraduate Enrollments This example uses the following data concerning Penn State undergraduate enrollment: Enrollment by Campus Campus If this is the case, follow the steps below. 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. In other words, the data file contained one row for each case. The topic of handling multiple sources of variation in statistical process control is beyond the scope of this blog.In the example above, raw data were used. Standard Shewhart control charts (Xbar-R or S) work when the within subgroup variation is dominant. If you do have subgroups of size greater than or equal to 2, you will have both within subgroup and between subgroup variation, a case of multiple sources of variation.
#Minitab express control charts series
NOTE: We focused on a series of individual data points, i.e. You can see a preview of the webinar here.
That webinar, titled SPC for Autocorrelated Data Using Automated Time Series Forecasting was given by John Noguera, Chief Technical Officer and Co-Founder of SigmaXL. However, the topic was addressed in one of ISSSP’s past webinars which is available to members of ISSSP. If the series is negatively autocorrelated, you must conduct a more sophisticated time series analysis using ARIMA. NOTE: What we just did was for a positively autocorrelated series. Having adjusted for autocorrelation, we see that the process is in control. Click OK and OK.Īs before, construct an I-MR chart pair using the residuals. We choose to construct a normal probability plot.Ĭhoose Distribution and Normal.
Plot points follow a normal probability distribution. Now let’s check the errors (residuals) to see if they are independent (uncorrelated) and are normally distributed. Choose Storage, Choose Residuals and click OK and OK.īecause you chose Storage, the residuals were added to your worksheet. Go to Stat>Time Series>Single Exp Smoothing.Ĭhoose Optimal ARIMA. If OK, construct the I-MR chart using the errors. We need to smooth the series using exponential smoothing and save the errors (the difference between the actual and fitted data).Ĭheck the errors for independence and normality. But, let’s see what the I-MR chart pair would look like if we corrected for the significant autocorrelation at lag 1.ĭoing this for positively autocorrelated data is usually quite easy. The Individual’s chart has several points out of control. We’ll just click OK without going to I-MR options at this time where we could have chosen the out-of-control tests. Go to Stat>Control Charts>Variable Charts for Individuals>I-MR Without adjusting for positive autocorrelation, let’s construct an individuals chart for the data. A down arrow beyond the limits would signify negative correlation. Once the data are loaded, go to Stat > Time Series > Autocorrelation.Īn up arrow beyond the limits indicates the data is positively correlated. Let’s check for this in Minitab 17 using the following dataset. If that condition is violated, we say the data is autocorrelated. Independence occurs when the position of one data point does not influence the position of the next data point. We will focus on the Individual’s Control Chart. What assumptions must be met to use a standard control chart? What should you do if one or more are not satisfied?