Regression when x and y each have uncertainties I have a set of $N$ points $(x_i,y_i)$. $X$ and $Y$ both have some noise associated with them due to measurement inaccuracy however the relationship of the underlying true values (i.e. if we could remove the noises) of these points should be of the form $y = mx +c$ where $m$ and $c$ are constants.
However, due to measurement inaccuracies in both $X$ and $Y$, I will get uncertainties in both my $m$ and $c$ values.
1)If I assume that my measurement inaccuracies for both $X$ and $Y$ are Gaussian distributed $\epsilon$ ~ $N(0,\sigma)$ how do I obtain the most likely $(m,c)$ and the uncertainties/confidence in both.
2) If I instead know that the uncertainties are different for $X$ and $Y$ such that $\sigma_x \neq \sigma_y$ where  $\epsilon_x$ ~ $N(0,\sigma_x)$, $\epsilon_y$ ~ $N(0,\sigma_y)$ can I get a different estimate of $(m,c)$ and the uncertainties/confidence in both.
 A: As a general concept the problem of error in X is called measurement error.
In linear regression analysis it causes attenuation bias, which is considered as one of the sources of engogeneity. Measuremet error shrinks coefficient of the right-hand-side variable measured with an error towards zero. It causes not uncertainty of an estimator, but its inconsistency instead.
While mentioned in other answer deming regression is two-variable concept, the multivariate solutions include instrumental variable method as preferred option.
Formulas for attenuation bias in case of linear regression are precisely derived, for example in here. This means if you have a clue of the variation of the error, you might estimate severity of the problem and possibly correct for it.
Measurement in Y, in case of linear regression is less of the problem, as linear regression assumes random error in the dependent variable. It causes worse prediction and higher residual variance, but do not bias coefficients in any way.

EDIT: This problem is directly described in Hausman (2001).

Hausman, Jerry. "Mismeasured variables in econometric analysis: problems from the right and problems from the left." Journal of Economic perspectives 15, no. 4 (2001): 57-67.
A: In both cases you want to use Deming regression. Case 1 is a special case of Deming regression called orthogonal regression, which minimizes the sum of squared perpendicular distances from the data points to the regression line. For case 2, the general case, you will need an estimate of the ratio $\delta = \sigma^2_y / \sigma^2_x$ for the problem to be solvable.
