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Searchjust y=ax+b [General Statistics]
» x1=c(1,2,3,4,5)
» y1=c(10.5, 11.4, 12.6, 13.3, 14.6)
»
» x2=c(1,2,3,4,5)
» y2=c(10.3, 11.4, NA, 13.5, 14.6)
»
13.3 vs. 13.5? By intention?
"RMSE
The RMSE is the square root of the variance of the residuals. It indicates the absolute fit of the model to the data–how close the observed data points are to the model’s predicted values. Whereas R-squared is a relative measure of fit, RMSE is an absolute measure of fit. As the square root of a variance, RMSE can be interpreted as the standard deviation of the unexplained variance, and has the useful property of being in the same units as the response variable. Lower values of RMSE indicate better fit. RMSE is a good measure of how accurately the model predicts the response, and is the most important criterion for fit if the main purpose of the model is prediction."
...educated guess, no idea if sensitive for number of calibrators
http://www.theanalysisfactor.com/assessing-the-fit-of-regression-models/
..didn't read it, maybe you want to have a look:
http://orfe.princeton.edu/~jqfan/papers/01/ant.pdf
» y1=c(10.5, 11.4, 12.6, 13.3, 14.6)
»
» x2=c(1,2,3,4,5)
» y2=c(10.3, 11.4, NA, 13.5, 14.6)
»
13.3 vs. 13.5? By intention?
"RMSE
The RMSE is the square root of the variance of the residuals. It indicates the absolute fit of the model to the data–how close the observed data points are to the model’s predicted values. Whereas R-squared is a relative measure of fit, RMSE is an absolute measure of fit. As the square root of a variance, RMSE can be interpreted as the standard deviation of the unexplained variance, and has the useful property of being in the same units as the response variable. Lower values of RMSE indicate better fit. RMSE is a good measure of how accurately the model predicts the response, and is the most important criterion for fit if the main purpose of the model is prediction."
...educated guess, no idea if sensitive for number of calibrators
http://www.theanalysisfactor.com/assessing-the-fit-of-regression-models/
..didn't read it, maybe you want to have a look:
http://orfe.princeton.edu/~jqfan/papers/01/ant.pdf
—
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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