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Searchjust y=ax+b [General Statistics]
Best guess: in case of unweighted linear regression the highest calibrators always have the highest impact. For your example: Take 138 dBA as the mean of the regression function, as predictions will be best arround the mean.
If there is heteroscedastic data and weighted regression, the calibrators arround the weighted mean will have least impact on the calibration function.
I still have not fully understood the scope of the survey. Is it on best practice of study planning for calibration experiments?
If there is heteroscedastic data and weighted regression, the calibrators arround the weighted mean will have least impact on the calibration function.
I still have not fully understood the scope of the survey. Is it on best practice of study planning for calibration experiments?
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Kindest regards, nobody
Kindest regards, nobody
Complete thread:
- Goodness of fits: one model, different datasets - ElMaestro, 2017-10-06 23:01 [General Statistics]
![[Mix]](https://static.bebac.at/img/mix.png "open in mix view")
- Goodness of fits: one model, different datasets - nobody, 2017-10-07 16:03
- Experimental setup, details - ElMaestro, 2017-10-07 18:06
- Visualization - ElMaestro, 2017-10-07 19:07
- multiple regression? - Helmut, 2017-10-08 17:17
- just y=ax+b - ElMaestro, 2017-10-08 17:30
- just y=ax+b - Helmut, 2017-10-08 17:35
- just y=ax+b - ElMaestro, 2017-10-08 17:50
- just y=ax+b - nobody, 2017-10-08 20:26
- ANCOVA with R? - yjlee168, 2017-10-08 21:28
- just y=ax+b - DavidManteigas, 2017-10-09 10:34
- just y=ax+b - nobody, 2017-10-09 10:45
- just y=ax+b - Helmut, 2017-10-10 18:15
- just y=ax+b - ElMaestro, 2017-10-08 17:50
- just y=ax+b - Helmut, 2017-10-08 17:35
- just y=ax+b - ElMaestro, 2017-10-08 17:30
- Experimental setup, details - ElMaestro, 2017-10-07 18:06
- Goodness of fits: one model, different datasets - nobody, 2017-10-07 16:03
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