Root mean square errors

The root-mean-square error is the square root of the mean square error. Note that since the root-mean-square error involves the square of the differences, outliers have more influence on this statistic than on the mean absolute error. 

Which measure of scatter is likely to be larger, the mean absolute error or the root-mean-square error ... 

The root-mean-square error (RMS error) is a statistic closely related to MAD for gaussian distributions. It provides a measure of the abso differences between calculated values and experiment as well as distribution of the values with respect to the mean. 

Figure 16 Root-mean-squared error progression plot for Fletcher nonlinear optimization and back-propagation algorithms during training.

For the data the squared correlation coefficient was 0.93 with a root mean square error of 2.2. The graph of predicted versus actual observed MS(1 +4) along with the summary of fit statistics and parameter estimates is shown in Figure 16.7. 

Root mean square error Mean of response Observations (or sum wt)... 

Figure 9. Linearity of response and reproducibility. The error flags indicate the root mean square error for five measurements at each value. The average relative error is about 10%.

Aptula AQ, Jeliazkova NG, Schultz TW, Cronin MTD. The better predictive model high q for the training set or low root mean square error of prediction for the test set QSAR Comb Sci 2005 24 385-96. 

Statishcal criteria of Eq. (24) are too good the standard deviation, which was created on the basis of different measurements by various authors, is much less than even the experimental error of determinahon. This could be due to mutual intercorrelation of descriptors leading to over-ophmistic statistics [18]. Another reason may be the lack of diversity in the training set. The applicahon of the solvation equation to data extracted from the MEDchem97 database gave much more modest results n = 8844, = 0.83, root mean square error = 0.674, F = 8416... 

Calculated at the JSCH-2005 database geometries [1]. (In parenthesis) number of comparisons. b Mean signed error. c Mean deviation. d Mean unsigned error. e Root mean square error. 

If these M points are randomly scattered near Z(i), then the average error is near zero (positive and negative errors are equally likely), but the average square error is nonzero. An accurate Taylor expansion could be defined as one for which the root-mean-square error is less than some tolerance, Etoi,... 

For the approximately 1,500 points in this range of uniform heat flux CHF experiments, the root-mean-square error was -10%. 

Number of test sections, 22 Number of data points, 638 Average ratio, 0.997 Root-mean-square error, 6.13%... 

The root-mean-square error in the kinetic fit was an acceptable 2.83% and was minimized by the SIMPLEX method discussed elsewhere (8). An... 

The ultrasound-assisted experiment of Figure 2(a) is again not typical in that the reagent concentrations have an inflection point mid-way through the reaction. We have performed a kinetic analyses of the stirred (blank) data in Figure 2(b) and found the following equations reproduce the data well with a root-mean-square error of 2.8% ... 

RMSEte Root mean square error for the test set RMSEtr Root mean square error for the training set SMILES Simplified molecular input line entry specification... 

QSPR Quantitative structure-property relationship RMSE Root mean squared error... 

A variety of statistical parameters have been reported in the QSAR literature to reflect the quality of the model. These measures give indications about how well the model fits existing data, i.e., they measure the explained variance of the target parameter y in the biological data. Some of the most common measures of regression are root mean squares error (rmse), standard error of estimates (s), and coefficient of determination (R2). 

We have compared one-step and two-step ahead scheduling using two performance measures. The first is the root mean square error of the track estimation this is a fairly obvious measure of the performance of the tracker. The second measure was the number of track updates. Since the sensor is managed in such a way that track updating is done only when the predicted track error exceeds a threshold, this also gives a measure of how far the estimation process is diverging from the actual target state. 

Figure 2. Root Mean Square Error (RMSE). Target 1...

Figure 4 Root Mean Square Error for Entropy Cost.

Root mean square (RMS) granularity, 19 264 Root-mean-squared error of cross-validation (RMSECV), 6 50-51 Root-mean-squared error of calibration (RMSEC), 6 50-51... 

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