patric wessels said:
perhaps I should elaborate a bit about the samples: we produce a foil with a coating applied on it. The mentioned 10 measured spots are 10 points across the foil (as in our standard inspection), these data are called one sample with its average, stdev etc.
From our production plant I have these 2 series of 8 samples, each serie with a different sample preparation. It is impossible for me to differ the sample preparation on a same sample. Once prepared it is "finished". But wwe know that if samples are taken close from eachother (and we have a slow and stable process) they are very much alike.
I want to know if the variation in 1 sample depends on the sample preparation method.
So I think what Steven says makes sence.
I have attached (part of) my data.
Best regards,
Patric
Do you mean to ask if the preparaton method can explain the variation in a sample since its not constant? Unless you can control the preparation or at least categorise it based on environmental elements like humidity or ambient temp etc then you just have to live with it, maybe some fractional factorial experiments can help you assess the interaction effect of preparation variation? I have some statistical process control charts on my website (95% complete) that I think would be of use...
R-chart: Calculates the upper and lower control limits for the variability in a process. The R-Chart is a control chart for processes where the variable can be measured rather than counted.
C-chart: Calculates the upper and lower control limits for the number of defects in a series of individual items. The c-Chart is a control chart for attributes. It is used when the quality characteristics of a process can be counted rather than measured.
P-chart: Calculates the upper and lower control limits for the number of defective items in a series of samples. Individual samples from the sample series, whose proportion of defective items (p) is above the upper control limit (UCL), can indicate that a process needs adjustment. The p-Chart is a control chart for attributes. It is used when the quality characteristic of a process is counted rather than measured.
We also do the X-chart. If you like give me the complete data set I will run it through for you. You really need to plot continuous processes over time to establish trends and measure variation that way instead of small isolated sample events to describe the difference. You cant really tell if the emultion used on the foil was chemically consistent for that sample and so on. None of this may be use to you, I guess I am just looking at it in another perspective. If so, sorry for my ramblings!
Andrew
B.Com (hons) M.Com (hons)