The r.m.s. error of the regression line for predicting $y$ from $x$ is$\begin y r \times \text x \\ x \sqrt \times \text y \\ r \times \operatorname \text y \sqrt \times \text x\end$ copy 2024 RedScores - All Rights Reserved. Correlation between CC correlation coefficient between experimental pKd or pKi and predicted binding… Distribution of the correlation coefficient between experimental pKi and predicted binding score from… Note: Pearson correlations between experimental affinities and the RF predictions are reported for the whole datasets but for values marked with ‘*’ that indicates values for a 20% test set. Favourites Shows 10 Max All Games LIVE Finished Odds The standard error of estimate indicates the accuracy of predictions made from the regression. The least square estimation is to minimize the sum of difference between the true Y and fitted Y.In a simple linear regression model, the parameters are unknownO In a simple linear regression model, the error term is unobserved.