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Registerjust 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+bnobody 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
Ing. Helmut Schütz