diff --git a/Statistics/LinearRegression.hs b/Statistics/LinearRegression.hs
--- a/Statistics/LinearRegression.hs
+++ b/Statistics/LinearRegression.hs
@@ -5,9 +5,12 @@
     linearRegression,
     linearRegressionRSqr,
     linearRegressionTLS,
-    -- * related functions
+    -- * Related functions
     correl,
     covar,
+    -- * Estimated errors and distribution parameters
+    linearRegressionMSE,
+    linearRegressionDistributions,
     -- * Robust linear regression
     robustFit,
     nonRandomRobustFit,
@@ -39,6 +42,9 @@
 import Data.List (minimumBy, sortBy)
 import Data.Maybe (fromMaybe)
 import qualified Statistics.Sample as S
+import qualified Statistics.Distribution as D
+import qualified Statistics.Distribution.Transform as T
+import qualified Statistics.Distribution.StudentT as ST
 
 --- * Simple linear regression
 
@@ -91,6 +97,28 @@
         (alpha, beta, _) = linearRegressionRSqr xs ys
 {-# INLINE linearRegression #-}
 
+-- | The error (or residual) mean square of a sample w.r.t. an estimated regression line.
+--   This serves as an estimate for the variance of the sampled data.
+--   Accepts the regression parameters (alpha,beta) and the sample vectors X and Y.
+linearRegressionMSE :: (Double,Double) -> S.Sample -> S.Sample -> Double
+linearRegressionMSE ab xs ys = (U.sum . U.map (linearRegressionError ab) . U.zip xs $ ys)/(n-2)
+    where
+        !n = fromIntegral $ U.length xs
+
+-- | The estimated distributions of the regression parameters (alpha and beta) assuming normal, identical distributions of Y, the sampled data.
+-- These can serve to get confidence intervals for the regression parameters.
+-- Accepts the regression parameters (alpha,beta) and the sample vectors X and Y.
+-- The distributions are StudnetT distributions centered at the estimated (alpha,beta) respectively, with parameter numbers n-2 (where n is the initial sample size) and with standard deviations that are extracted from the sampled data based on its MSE. See chapter 2 of reference [3] for details.
+linearRegressionDistributions :: (Double,Double) -> S.Sample -> S.Sample -> (T.LinearTransform ST.StudentT,T.LinearTransform ST.StudentT)
+linearRegressionDistributions (alpha,beta) xs ys = (ST.studentTUnstandardized (n-2) alpha va,ST.studentTUnstandardized (n-2) beta vb)
+    where
+        !n = fromIntegral $ U.length xs
+        !mse = linearRegressionMSE (alpha,beta) xs ys
+        !vb = mse/(xv)
+        !mx = S.mean xs
+        !va = mse*(1/n+mx^2/xv)
+        !xv = U.sum . U.map (\x -> (x-mx)^2) $ xs
+
 -- | Total Least Squares (TLS) linear regression.
 -- Assumes x-axis values (and not just y-axis values) are random variables and that both variables have similar distributions.
 -- interface is the same as 'linearRegression'.
@@ -291,4 +319,5 @@
 -- * Two Dimensional Euclidean Regression (Stein) <http://www.dspcsp.com/pubs/euclreg.pdf>
 --
 -- * Computing LTS Regression For Large Data Sets (Rousseeuw and Driessen) <http://agoras.ua.ac.be/abstract/Comlts99.htm>
-
+--
+-- * Applied linear statistical models (Kutner et al.)
diff --git a/statistics-linreg.cabal b/statistics-linreg.cabal
--- a/statistics-linreg.cabal
+++ b/statistics-linreg.cabal
@@ -1,8 +1,10 @@
 Name:                statistics-linreg
-Version:             0.2.3
+Version:             0.2.4
 Synopsis:            Linear regression between two samples, based on the 'statistics' package.
 Description:         Provides functions to perform a linear regression between 2 samples, see the documentation of the linearRegression functions. This library is based on the 'statistics' package.
 		     .
+		       * 0.2.4: added distribution estimations for standard regression parameters.
+		     .
 		       * 0.2.3: added robust-fit support.
 		     .
 		       * 0.2.2: added the Total-Least-Squares version and made some refactoring to eliminate code duplication
@@ -31,7 +33,7 @@
 License-file:        LICENSE
 Author:              Alp Mestanogullari <alpmestan@gmail.com>, Uri Barenholz <uri.barenholz@weizmann.ac.il>
 Maintainer:          Alp Mestanogullari <alpmestan@gmail.com>
-Copyright:           2010-2012 Alp Mestanogullari
+Copyright:           2010-2013 Alp Mestanogullari
 Stability:           Experimental
 Category:            Math, Statistics
 Build-type:          Simple