%% Temple university CIS 2033
% A modern Introduction to Probability and Statistics
% Chapter 12: The Poisson process
% Chapter 15: Data analysis: graphical summaries 
% Chapter 16: Data analysis: numerical summaries
%
% Author: Djordje Gligorijevic
% email: gligorijevic@temple.edu
%
% Special thanks to Min Xiao (http://astro.temple.edu/~tud09663/index.html)
% for given examples and ideas for lab examples.

%% Excercise 1: load poiss.mat. 
% Let the discrete random variable $X$ be the number of customers visiting
% ABC Bank in a hour. We know that $X ~ Poiss(lambda). 
% The poiss.mat stores such information: 1000 records. 
% 1.1) Estimate lambda;
% 1.2) Based on the computed lambda, randomly generate 500 data samples from
% Poiss(lambda)
% 1.3) Save the data into a mat file. 


%% Excercise 2: load norm.mat. 
% Let the continuous random variable $X$ be the price changes of the product XYZ. 
% Positive values mean its price is increased and negative values means its
% price is decreased. 
% We know that X ~ N(mu, sigma^2). 
% The norm.mat stores 1000 records of such information. 
% Please load the data and 
% 2.1) Estimate mu and sigma;
% 2.2) Based on the computed mu and sigma, randomly generate 500 data
% samples from N(mu, sigma^2)
% 2.3) Save the data into a mat file. 


%% Excercise 3: load oldfaithful.txt
% Replicate figure 15.2 from the Textbook, page 211.


%% Excercise 4: load oldfaithful.txt and software.txt data
% Replicate Figure 15.10 from the textbook page 220


%% Excercise 5: load drilling.txt
% Replicate Figure 15.10 from the textbook page 223


%% Excercise 6: load jankahardness.txt
% Replicate Figure 15.12 from the textbook page 224

