added more text to regression analysis

doc/src/Regression/Regression.do.txt

@@ -7,7 +7,7 @@ DATE: today
 ===== Regression analysis, overarching aims  =====
 !bblock
 
-Regression modeling deals with the description of  the sampling distribution of a given random variable $y$ varies as function of another variable or a set of such variables $\hat{x} =[x_0, x_1,\dots, x_p]$. 
+Regression modeling deals with the description of  the sampling distribution of a given random variable $y$ varies as function of another variable or a set of such variables $\hat{x} =[x_0, x_1,\dots, x_p]^T$. 
 The first variable is called the _dependent_, the _outcome_ or the _response_ variable while the set of variables $\hat{x}$ is called the independent variable, or the predictor variable or the explanatory variable. 
  
 A regression model aims at finding a likelihood function $p(y\vert \hat{x})$, that is the conditional distribution for $y$ with a given $\hat{x}$. The estimation of  $p(y\vert \hat{x})$ is made using a data set with 
@@ -21,6 +21,8 @@ A regression model aims at finding a likelihood function $p(y\vert \hat{x})$, th
 !split
 ===== General linear models  =====
 !bblock
-more text to come
+Before we proceed let us study a case from linear algebra where we aim at fitting a set of data $\hat{y}=[y_0,y_1,\dots,y_{n-1}]$. We could think of these data as a result of an experiment or a complicated numerical experiment. These data are functions of a series of variables $\hat{x}=[x_0,x_1,\dots,x_{n-1}]$, that is $y_i = y(x_i)$ with $i=0,1,2,\dots,n-1$. The variables $x_i$ could represent physical quantities like time, temperature, position etc. We assume that $y(x)$ is a smooth function. 
+
+Since obtaining these data points may not be trivial, we want to use these data to fit a function which can allow us to make predictions for values of $y$ which are not in the present set. The perhaps simplest approach is to assume we can parametrize 
 
 !eblock