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diff --git a/statistics/quickref.tex b/statistics/quickref.tex
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+Creating a linear array:\\
+{\ex \lstinline|    x = linspace(0, 2*pi, 50)|}
+
+Plotting two variables:\\
+{\ex \lstinline|    plot(x, sin(x))|}
+
+Plotting two lists of equal length x, y:\\
+{\ex \lstinline|    plot(x, y)|}
diff --git a/statistics/script.rst b/statistics/script.rst
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+Hello friends and welcome to the tutorial on statistics using Python
+
+{{{ Show the slide containing title }}}
+
+{{{ Show the slide containing the outline slide }}}
+
+In this tutorial, we shall learn
+ * Doing simple statistical operations in Python  
+ * Applying these to real world problems 
+
+You will need Ipython with pylab running on your computer
+to use this tutorial.
+
+Also you will need to know about loading data using loadtxt to be 
+able to follow the real world application.
+
+We will first start with the most necessary statistical 
+operation i.e finding mean.
+
+We have a list of ages of a random group of people ::
+   
+   age_list=[4,45,23,34,34,38,65,42,32,7]
+
+One way of getting the mean could be getting sum of 
+all the elements and dividing by length of the list.::
+
+    sum_age_list =sum(age_list)
+
+sum function gives us the sum of the elements.::
+
+    mean_using_sum=float(sum_age_list)/len(age_list)
+
+This obviously gives the mean age but python has another 
+method for getting the mean. This is the mean function::
+
+       mean(age_list)
+
+Mean can be used in more ways in case of 2 dimensional lists.
+Take a two dimensional list ::
+     
+     two_dimension=[[1,5,6,8],[1,3,4,5]]
+
+the mean function used in default manner will give the mean of the 
+flattened sequence. Flattened sequence means the two lists taken 
+as if it was a single list of elements ::
+
+    mean(two_dimension)
+    flattened_seq=[1,5,6,8,1,3,4,5]
+    mean(flattened_seq)
+
+As you can see both the results are same. The other way is mean 
+of each column.::
+   
+   mean(two_dimension,0)
+   array([ 1. ,  4. ,  5. ,  6.5])
+
+we pass an extra argument 0 in that case.
+
+In case of getting mean along the rows the argument is 1::
+   
+   mean(two_dimension,1)
+   array([ 5.  ,  3.25])
+
+We can see more option of mean using ::
+   
+   mean?
+
+Similarly we can calculate median and stanard deviation of a list
+using the functions median and std::
+      
+      median(age_list)
+      std(age_list)
+
+Median and std can also be calculated for two dimensional arrays along columns and rows just like mean.
+
+       For example ::
+       
+       median(two_dimension,0)
+       std(two_dimension,1)
+
+This gives us the median along the colums and standard devition along the rows.
+       
+Now lets apply this to a real world example 
+    
+We will a data file that is at the a path
+``/home/fossee/sslc2.txt``.It contains record of students and their
+performance in one of the State Secondary Board Examination. It has
+180, 000 lines of record. We are going to read it and process this
+data.  We can see the content of file by double clicking on it. It
+might take some time to open since it is quite a large file.  Please
+don't edit the data.  This file has a particular structure.
+
+We can do ::
+   
+   cat /home/fossee/sslc2.txt
+
+to check the contents of the file.
+
+Each line in the file is a set of 11 fields separated 
+by semi-colons Consider a sample line from this file.  
+A;015163;JOSEPH RAJ S;083;042;47;00;72;244;;; 
+
+The following are the fields in any given line.
+* Region Code which is 'A'
+* Roll Number 015163
+* Name JOSEPH RAJ S
+* Marks of 5 subjects: ** English 083 ** Hindi 042 ** Maths 47 **
+Science AA (Absent) ** Social 72
+* Total marks 244
+*
+
+Now lets try and find the mean of English marks of all students.
+
+For this we do. ::
+
+     L=loadtxt('/home/fossee/sslc2.txt',usecols=(3,),delimiter=';')
+     L
+     mean(L)
+
+loadtxt function loads data from an external file.Delimiter specifies
+the kind of character are the fields of data seperated by. 
+usecols specifies  the columns to be used so (3,). The 'comma' is added
+because usecols is a sequence.
+
+To get the median marks. ::
+   
+   median(L)
+   
+Standard deviation. ::
+	
+	std(L)
+
+
+Now lets try and and get the mean for all the subjects ::
+
+     L=loadtxt('/home/fossee/sslc2.txt',usecols=(3,4,5,6,7),delimiter=';')
+     mean(L,0)
+     array([ 73.55452504,  53.79828941,  62.83342759,  50.69806158,  63.17056881])
+
+As we can see from the result mean(L,0). The resultant sequence  
+is the mean marks of all students that gave the exam for the five subjects.
+
+and ::
+    
+    mean(L,1)
+
+    
+is the average accumalative marks of individual students. Clearly, mean(L,0)
+was a row wise calcultaion while mean(L,1) was a column wise calculation.
+
+
+{{{ Show summary slide }}}
+
+This brings us to the end of the tutorial.
+we have learnt
+
+ * How to do the standard statistical operations sum , mean
+   median and standard deviation in Python.
+ * Combine text loading and the statistical operation to solve
+   real world problems.
+
+{{{ Show the "sponsored by FOSSEE" slide }}}
+
+
+This tutorial was created as a part of FOSSEE project, NME ICT, MHRD India
+
+Hope you have enjoyed and found it useful.
+Thankyou
+ 
+.. Author              : Amit Sethi
+   Internal Reviewer 1 : 
+   Internal Reviewer 2 : 
+   External Reviewer   :
+
diff --git a/statistics/slides.org b/statistics/slides.org
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+#+LaTeX_CLASS: beamer
+#+LaTeX_CLASS_OPTIONS: [presentation]
+#+BEAMER_FRAME_LEVEL: 1
+
+#+BEAMER_HEADER_EXTRA: \usetheme{Warsaw}\useoutertheme{infolines}\usecolortheme{default}\setbeamercovered{transparent}
+#+COLUMNS: %45ITEM %10BEAMER_env(Env) %10BEAMER_envargs(Env Args) %4BEAMER_col(Col) %8BEAMER_extra(Extra)
+#+PROPERTY: BEAMER_col_ALL 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 :ETC
+#+OPTIONS:   H:5 num:t toc:nil \n:nil @:t ::t |:t ^:t -:t f:t *:t <:t
+
+#+TITLE: Statistics
+#+AUTHOR: FOSSEE
+#+DATE: 2010-09-14 Tue
+#+EMAIL:     info@fossee.in
+
+# \author[FOSSEE] {FOSSEE}
+
+# \institute[IIT Bombay] {Department of Aerospace Engineering\\IIT Bombay}
+# \date{}
+
+* Tutorial Plan 
+** Doing simple statistical operations in Python  
+** Using loadtxt to solve statistics problem
+
+* Summary 
+**  seq=[1,5,6,8,1,3,4,5]
+**  sum(seq)
+**  mean(seq)
+**  median(seq)
+**  std(seq)
+
+* Summary
+
+** loadtxt
diff --git a/statistics/slides.tex b/statistics/slides.tex
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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Tutorial slides on Python.
+%
+% Author: FOSSEE 
+% Copyright (c) 2009, FOSSEE, IIT Bombay
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\documentclass[14pt,compress]{beamer}
+%\documentclass[draft]{beamer}
+%\documentclass[compress,handout]{beamer}
+%\usepackage{pgfpages} 
+%\pgfpagesuselayout{2 on 1}[a4paper,border shrink=5mm]
+
+% Modified from: generic-ornate-15min-45min.de.tex
+\mode<presentation>
+{
+  \usetheme{Warsaw}
+  \useoutertheme{infolines}
+  \setbeamercovered{transparent}
+}
+
+\usepackage[english]{babel}
+\usepackage[latin1]{inputenc}
+%\usepackage{times}
+\usepackage[T1]{fontenc}
+
+\usepackage{ae,aecompl}
+\usepackage{mathpazo,courier,euler}
+\usepackage[scaled=.95]{helvet}
+
+\definecolor{darkgreen}{rgb}{0,0.5,0}
+
+\usepackage{listings}
+\lstset{language=Python,
+    basicstyle=\ttfamily\bfseries,
+    commentstyle=\color{red}\itshape,
+  stringstyle=\color{darkgreen},
+  showstringspaces=false,
+  keywordstyle=\color{blue}\bfseries}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Macros
+\setbeamercolor{emphbar}{bg=blue!20, fg=black}
+\newcommand{\emphbar}[1]
+{\begin{beamercolorbox}[rounded=true]{emphbar} 
+      {#1}
+ \end{beamercolorbox}
+}
+\newcounter{time}
+\setcounter{time}{0}
+\newcommand{\inctime}[1]{\addtocounter{time}{#1}{\tiny \thetime\ m}}
+
+\newcommand{\typ}[1]{\lstinline{#1}}
+
+\newcommand{\kwrd}[1]{ \texttt{\textbf{\color{blue}{#1}}}  }
+
+% Title page
+\title{Your Title Here}
+
+\author[FOSSEE] {FOSSEE}
+
+\institute[IIT Bombay] {Department of Aerospace Engineering\\IIT Bombay}
+\date{}
+
+% DOCUMENT STARTS
+\begin{document}
+
+\begin{frame}
+  \maketitle
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Outline}
+  \begin{itemize}
+    \item 
+  \end{itemize}
+\end{frame}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%              All other slides here.                  %%
+%% The same slides will be used in a classroom setting. %% 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\begin{frame}[fragile]
+  \frametitle{Summary}
+  \begin{itemize}
+    \item 
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Thank you!}  
+  \begin{block}{}
+  \begin{center}
+  This spoken tutorial has been produced by the
+  \textcolor{blue}{FOSSEE} team, which is funded by the 
+  \end{center}
+  \begin{center}
+    \textcolor{blue}{National Mission on Education through \\
+      Information \& Communication Technology \\ 
+      MHRD, Govt. of India}.
+  \end{center}  
+  \end{block}
+\end{frame}
+
+\end{document}