--- a/lstsq.rst	Wed Sep 22 14:48:44 2010 +0530
+++ b/lstsq.rst	Wed Sep 22 15:22:21 2010 +0530
@@ -1,3 +1,8 @@
+.. Author              : Nishanth
+   Internal Reviewer 1 : Puneeth
+   Internal Reviewer 2 : 
+   External Reviewer   :
+
 Hello friends and welcome to the tutorial on Least Square Fit
 
 {{{ Show the slide containing title }}}
@@ -17,31 +22,14 @@
 pendulum and the second is the corresponding time period of the pendulum.
 
 As we know, the square of time period of a pendulum is directly proportional to
-its length, we shall plot l vs t^2 and verify if the proportionality is linear.
-
-If it is not linear, we shall generate a least square fit line.
-
-{{{ show the slide containing explanation on least square fit }}}
-
-As shown in the slide, we are first going to generate the two matrices tsq and
-A. Then we are going to use the =lstsq= function to find the values of m and c.
-
-To read the input file and parse the data, we are going to loadtxt function.
-Type
-::
+its length, we shall plot l vs t^2 and verify this. 
 
-    data = loadtxt("/home/fossee/pendulum.txt")
-    data
+#[Puneeth:] removed the explanation about loadtxt and unpack
+ option. It's been done in another LO already. simple dependency 
+ should work?
 
-As you can see, data is a sequence containing 90 records. Each record contains
-two values. The first is length and second is time period. But what we need is 
-two sequences. One sequence containing all the length values and one containing
-all the time values.
-
-Hence we have to use the unpack option with loadtxt. It unpacks the data into
- sequences depending on the structure of data.
-
-Type
+To read the input file and parse the data, we are going to use the
+loadtxt function.  Type 
 ::
 
     l, t = loadtxt("/home/fossee/pendulum.txt", unpack=True)
@@ -57,10 +45,20 @@
     tsq = t * t
     plot(l, tsq, 'bo')
 
-
 {{{ switch to the plot window }}}
 
-We can see that there is a visible linear trend.
+#[Puneeth:] Moved explanation of least square fit here. seems more
+apt. 
+
+We can see that there is a visible linear trend, but we do not get a
+straight line connecting them. We shall, therefore, generate a least
+square fit line.
+
+{{{ show the slide containing explanation on least square fit }}}
+
+As shown in the slide, we are first going to generate the two matrices
+tsq and A. Then we are going to use the ``lstsq`` function to find the
+values of m and c.
 
 let us now generate the A matrix with l values.
 We shall first generate a 2 x 90 matrix with the first row as l values and the
@@ -70,20 +68,20 @@
     inter_mat = array((l, ones_like(l)))
     inter_mat
 
-We see that we have intermediate matrix. Now we need the transpose.Type
+We see that we have intermediate matrix. Now we need the transpose. Type
 ::
 
     A = inter_mat.T
     A
 
-Now we have both the matrices A and tsq. We only need to use the =lstsq=
+Now we have both the matrices A and tsq. We only need to use the ``lstsq``
 Type
 ::
 
     result = lstsq(A, tsq)
 
-The result is a sequence of values. The first item is the matrix p or in simple
-words, the values of m and c. Hence, 
+The result is a sequence of values. The first item in this sequence,
+is the matrix p i.e., the values of m and c. Hence, 
 ::
 
     m, c = result[0]
@@ -120,9 +118,5 @@
 This tutorial was created as a part of FOSSEE project, NME ICT, MHRD India
 
 Hope you have enjoyed and found it useful.
-Thankyou
+Thank you
  
-.. Author              : Nishanth
-   Internal Reviewer 1 : 
-   Internal Reviewer 2 : 
-   External Reviewer   :