Can anyone please help me find the outliers in my data?
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So I did an Elisa lab experiment and im confused about which bits of my data are outliers in my patient samples and my standards. Can anyone please help me out?
Standards:
0.0454 0.0407 0.0489
0.0581 0.0582 0.0541
0.0720 0.0626 0.0593
0.0900 0.0880 0.0722
0.1297 0.1341 0.1036
0.2264 0.1782 0.1787
0.3377 0.2281 0.2413
0.0377 0.0291 0.0343
Patient:
0.0625 0.0609 0.0623
0.2391 0.0347* 0.2967
0.4204 0.2973 0.3202
0.0487 0.0519 0.0517
0.0570 0.0542 0.0506
0.1417 0.1294 0.1375
0.0428 0.0436 0.0467
0.3075 0.1963 0.2133
* is where i think the outlier is, please correct me if im wrong.
Thanks.
Standards:
0.0454 0.0407 0.0489
0.0581 0.0582 0.0541
0.0720 0.0626 0.0593
0.0900 0.0880 0.0722
0.1297 0.1341 0.1036
0.2264 0.1782 0.1787
0.3377 0.2281 0.2413
0.0377 0.0291 0.0343
Patient:
0.0625 0.0609 0.0623
0.2391 0.0347* 0.2967
0.4204 0.2973 0.3202
0.0487 0.0519 0.0517
0.0570 0.0542 0.0506
0.1417 0.1294 0.1375
0.0428 0.0436 0.0467
0.3075 0.1963 0.2133
* is where i think the outlier is, please correct me if im wrong.
Thanks.
Last edited by Loreto2018; 1 month ago
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#2
I don't understand the data, but a common method of finding outliers is >upper quartile + 1.5*IQR or <lower quartile - 1.5*IQR
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#3
(Original post by Loreto2018)
So I did an Elisa lab experiment and im confused about which bits of my data are outliers in my patient samples and my standards. Can anyone please help me out?
Standards:
0.0454 0.0407 0.489
0.0581 0.582 0.0541
0.0720 0.0626 0.0593
0.0900 0.0880 0.0722
0.1297 0.1341 0.1036
0.2264 0.1782 0.1787
0.3377 0.2281 0.2413
0.0377 0.0291 0.0343
Patient:
0.0625 0.0609 0.0623
0.2391 0.0347* 0.2967
0.4204 0.2973 0.3202
0.0487 00519 0.0517
0.0570 0.0542 0.0506
0.1417 0.1294 0.1375
0.0428 0.0436 0.0467
0.3075 0.1963 0.2133
* is where i think the outlier is, please correct me if im wrong.
Thanks.
So I did an Elisa lab experiment and im confused about which bits of my data are outliers in my patient samples and my standards. Can anyone please help me out?
Standards:
0.0454 0.0407 0.489
0.0581 0.582 0.0541
0.0720 0.0626 0.0593
0.0900 0.0880 0.0722
0.1297 0.1341 0.1036
0.2264 0.1782 0.1787
0.3377 0.2281 0.2413
0.0377 0.0291 0.0343
Patient:
0.0625 0.0609 0.0623
0.2391 0.0347* 0.2967
0.4204 0.2973 0.3202
0.0487 00519 0.0517
0.0570 0.0542 0.0506
0.1417 0.1294 0.1375
0.0428 0.0436 0.0467
0.3075 0.1963 0.2133
* is where i think the outlier is, please correct me if im wrong.
Thanks.
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(Original post by Theloniouss)
I don't understand the data, but a common method of finding outliers is >upper quartile + 1.5*IQR or <lower quartile - 1.5*IQR
I don't understand the data, but a common method of finding outliers is >upper quartile + 1.5*IQR or <lower quartile - 1.5*IQR
Last edited by Loreto2018; 1 month ago
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(Original post by Jpw1097)
Can you correct the errors in your data. It seems as though you have put an extra ‘0’ here and there, if not, you have more outliers.
Can you correct the errors in your data. It seems as though you have put an extra ‘0’ here and there, if not, you have more outliers.
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(Original post by Theloniouss)
I don't understand the data, but a common method of finding outliers is >upper quartile + 1.5*IQR or <lower quartile - 1.5*IQR
I don't understand the data, but a common method of finding outliers is >upper quartile + 1.5*IQR or <lower quartile - 1.5*IQR
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#7
(Original post by Loreto2018)
So there are 8 triplicates (3 values) for both the patient and standards. And for each triplicate I need to find the average but first I have to exclude any outliers within each triplicate. I'm just having trouble identifying which ones would be outliers.
So there are 8 triplicates (3 values) for both the patient and standards. And for each triplicate I need to find the average but first I have to exclude any outliers within each triplicate. I'm just having trouble identifying which ones would be outliers.
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(Original post by Theloniouss)
I'd say the one you've identified looks like an outlier, but with only three data points (and if you're excluding the outlier, two) it's not really possible to say.
I'd say the one you've identified looks like an outlier, but with only three data points (and if you're excluding the outlier, two) it's not really possible to say.
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#9
(Original post by Loreto2018)
That's what i thought but when i calculated the LB for the data sets 0.2391, 0.0347, 0.2967 i got a value of -0.0596 and the UB was 0.4644 and all of these values are within range which means that it wouldn't be an outlier right?
That's what i thought but when i calculated the LB for the data sets 0.2391, 0.0347, 0.2967 i got a value of -0.0596 and the UB was 0.4644 and all of these values are within range which means that it wouldn't be an outlier right?
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(Original post by Theloniouss)
As I said, it's not really possible to know. By that definition it might not be, but there isn't really a single definition of an outlier and it depends on why you're looking for outliers and so on.
As I said, it's not really possible to know. By that definition it might not be, but there isn't really a single definition of an outlier and it depends on why you're looking for outliers and so on.
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