## just 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]
- 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