Estimating values knowing their Pearson's r and their means and standard deviations I apologize if my question is exceedingly simple. Imagine, for example, I am studying a paper which explains that two variables correlate with a certain Pearson's r (no p-value, no confidence interval, no sample size provided).
Can I estimate the value of one of the two variables if I know the other one? How?
I found some examples but all dealt with the knowledge of all the data, not just the final Pearson's r.
(I use R)
Edit: added one more question
Can I estimate the value of one of the two variables if I know the other one and their means and standard deviations? How?
 A: In simple linear regression, using the model $Y_i = \beta_0 + \beta_1x_i + e_i,$ where $e_i$ are independently $\mathsf{Norm}(0,\sigma),$ one can find estimates of $\beta_0,$ $\beta_1,$ and $\sigma$ knowing $n, \bar x, \bar  Y, S_x, S_y,$ and $r.$ 
Most of the formulas can be found in any elementary text that covers simple linear regression. For example, the slope is estimated as $\hat \beta_1 = rS_y/S_x.$ From there, $\hat \beta_0$ can be found by noting that the regression line must pass through $(\bar x, \bar Y).$ 
With $\hat \beta_0$ and $\hat \beta_1,$ one can find the point estimate $\hat Y_i$ corresponding to $x_i.$ Knowing $\hat \sigma^2 = S_{Y|x}^2,$ one can find a confidence interval corresponding to that point estimate.
Some additional formulas for confidence and prediction intervals also require 
$$S_{Y|x}^2 = \frac{n-1}{n-2}S_Y^2(1 - r^2),$$
where, by definition, $S_{Y|x}^2 = \frac{1}{n-2}\sum_{i=1}^n(\hat Y_i - Y_i)^2.$
However, 'regression diagnostics' (including residual plots and identification of influential points) are important to understanding whether the assumptions of
the regression model are valid. Most of these cannot be done without access to the data.
