Thank you so much for explaining this concept better ....
@xlisgr8
Ай бұрын
That is so nice. Within 10 minutes, had an idea about various Control Charts. Thank you.
@datatab
Ай бұрын
Glad it was helpful!
@xlisgr8
Ай бұрын
Please explain about Six Sigma concept also with reference to statistical measures.
@datatab
Ай бұрын
Many thanks! I will put it on my to do list!
@abdelgaderalfallah
Ай бұрын
Splendid 🎉🎉🎉
@datatab
Ай бұрын
Thanks : )
@brudo5056
29 күн бұрын
Hello, nice overvieuw video. In the example of the XBarChart you take 25 data points (each containing 5 samples a day) so in fact the UCL/LCL are based on 5x 25 samples = 125 samples Yes? But suppose in a laboratory certain analysis runs on one daily run with control standards/samples ... so 1 datapoint/day 1. How many samples (days) would you take to calculate the Mean, StdDev and UCL/LCL ? Should you take 30 as a minimum (30 is the border between T-distr. vs. Normal distr.) or do you take another statistical limit ? 2. When, in time, there is a clear indication of a shift of the mean (up or down...) how to react in a correct way for recalculation of Mean, StdDev, UCL/LCL etc... ?
@mohammedelbarbary8708
Ай бұрын
Thank you very much...Are these tests important in medical statistics?!
@datatab
Ай бұрын
Hi, many thanks for your feedback! Yes, Control charts help track and monitor the performance of healthcare processes over time. This could include patient wait times, surgical outcomes, infection rates, or medication errors. By identifying variations in these processes, healthcare providers can detect and correct issues, leading to improved quality of care. Regards Hannah
@tais51534
Ай бұрын
Control charts are also used in biochemical laboratories for analyzing biological samples (blood, urine, etc.). Every day, in parallel with the patients tests, the same tests are performed on 'standard samples'(usually provided by the manufacturer of the test kit with a known level of measured substance). The results of daily tests of these 'standard samples' are also plotted on the charts and ideally for each test result should fall within the 2 sigma interval. If there is a consistent tendency for results to be over- or under-estimated within 2 sigma, either the measurement device or the performance of the reagents should be checked/calibrated. If any standard sample result is outside 2 sigma, all patient results for that test (say total protein test or glucose test, etc.) become invalid because they are either overestimated when the standard sample result is above 2 sigma or underestimated when the standard sample result is below 2 sigma. Typically on such a day, all processes that may have gone wrong for that particular test/reagents are checked, the standard sample result is re-measured and ensured to be within the 2 sigma interval, and then all patient samples are re-measured.
@mohammedelbarbary8708
Ай бұрын
@@datatab Thank you very much for your illustration.💐
@mohammedelbarbary8708
Ай бұрын
@@tais51534 Thank you very much for your illustration.💐💐
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