Computational Statistics CS 5961/6961

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Class Information

Term: Spring 2011
Class Times: TH 14:00 - 15:20 – in WEB 1248
Instructor: Rich Riesenfeld  < rfr@cs.utah.edu >

Office: 2897 WEB
Office Hours: After class or by appointment.
Telephone: 801-581-7026

Mailing list:  cs5961@list.eng.utah.edu

Textbook (optional):

John Freund's Mathematical Statistics with Applications
Irwin Miller and Maryless Miller (2007)
ISBN-13: 978-0131427068

Course Description:  Press Release

Note:

The University of Utah seeks to provide equal access to its programs, services and activities for people with disabilities. If you will need accommodations in the class, reasonable prior notice needs to be given to the Center for Disability Services, 162 Olpin Union Building, 581-5020 (V/TDD). CDS will work with you and the instructor to make arrangements for accommodations.

All written information in this course can be made available in alternative format with prior notification to the Center for Disability Services.

Syllabus

Wk Date  Topic   Remarks
1.1

11 Jan

 Probability v statistics; continuous v discrete random variables; Bernoulli trials & coin flips; Combinations and Permutations; Counting.
1.2
13
 Basics: random variable, probability density distributions, cumulative distributions; event space & trials; Fundamental Thm of Calculus
   
2.1
18
 Binomial distribution & repeated events; joint distribution; independent and dependent events
2.2
20
 Common distribution: uniform, binomial, exponential, normal/gaussian; Pascal's triangle and binomial coefficients
   
3.1
25
 Counting principles and methods; probability calculation using event space numeration; combinatorics and probability
3.2
27
 Binomial coefficient calculations/identities
4.1
1 Feb
Statistical Questions using Binomial Dist
4.2
3
Level of Confidence Questions; Statistical Estimators
   
5.1
8
Mean and Expectation in probability and statistics; Law of Large Numbers; (binomial dist)

Hmwk1: Combinatorics
5.2
10
 Variance in probability and statistics; Var(binomial dist)
6.1
15
 Quiz on Basic Definintions; Go over Quiz
6.2
17
 Normal Distribution
 
7.1
22
 Normal Distribution  
7.2
24
Risk Analysis and Expected Value
8.1
 1 Mar
Convolution: Joint Distributions, Filters, Data Smoothing
8.2
3
Transformations, Eigenvalues, Eigenvectors; Revisit PCA
Postponed
9.1
8
 Demos and discussions of examples of convolutions
9.2
10
 Convolutions, for discrete and continuous

10.1
15
 Statistical Analysis Methodology: Null Hypothesis

Hmwk: Normal Distribution<doc>  <pdf>

10.2
17

Finish Probability; Poisson distribution

Hmwk due Fri, 18 Feb: Normal Dist

11.1
29
 Quiz on Basic Defs and Apps: Normal Dist, Poisson Dist
   
11.2
 31
Project Topic Presentatons

Topic and Plan for Final Project Due 31 Mar
12.1
 5 Apr

Statististical Testing; Level of Confidence;

   
12.2
7
Examples: Statistical Analysis 
   
13.1
12

Statistical Population Sampling

Approach and Methodology for Final Project Due 12 Apr
13.2
14
 Hypothesis Testing
   
14.1
19
 Sampling Theory
   
14.2
21
 Project Presentations
   
15.1

26

Project Presentations

Final Project due in Full 26 Apr
 

Lecture Notes

Selected Textbook Pages:

Textbook pages 1-15       pages 15-21       pages 23-68

Feller Vol 1, Ch 2, pages 52-64, Combinatorics Problems

Selected Basic Definitions:

Probability distribution functions

Convolutions

Conditional Probability:

Clearly Presented Conditional Probability Examples

Simple Conditional Probability Example w Applet

The Monty Hall Problem:

Let's Make a Deal Website (Monty Hall Problem): Background and Applet

Let's Make a Deal (Monty Hall) Applet

Nice Monty Hall Problem Demo Applet

Monty Hall Problem Data Simulator Applet

Monty Hall Problem discussed and analyzed (from the curious incident of the dog in the night-time by mark haddon)

And Behind Door No. 1, a Fatal Flaw (NYT 8 Apr 2008)

 

Expectation of Joint Distribution: 2 Dice:

Expectation for Sum of 2 Dice (rfr)

Notes on Joint Distributions and Convolution (rfr)

 

Binomial Distribution

Derivation Recursive Relationship for Binomial Coefficients

Derivation of E(X) and Var(X) for Binomial Distribution

 

Poisson Distribution

Derivation of Poisson Distribution as Limit of Binomial

 

Risk Analysis

Risk Analysis and Expected Value

 

Convolutions

Convolutions: Discrete and Continuous

 

Normal Distribution Illustrations:

Ball Drop

Flexible Normal Curve Shapes and Histogram Relationship

Interactive Normal Curve Shapes (go to bottom of target page)

Interactive Cumulative Area (Probability) Intervals

 

Linear Regression Analysis

Linear Regression (Yale Notes)

Linear Regression (Wikipedia)

Assumptions of Linear Regression

Linear Regression and Excel (Clemson Notes)

 

Statistical Analysis

Covariant/Correlation Notes:    .pptx    .ppt (compatibility mode)

Covariant/Correlation Notes:    .1up.color.pdf    .2up.color.pdf    .2up.b/w.pdf

Statistical Analysis

Investigation Strategy.pdf

Investigation Stategy.doc

Resources

Binomial Distribution Calculator

Calculus Material:

Anti-Derivative

Fundamental Theorem of Calculus (short)

Fundamental Theorem of Calculus (bis; longer development)

Mean Value Theorem

 

Using a Priori Information:

Bayes' Theorem

 

Markov Chain Links:

Definition of Markov Chain

Markov Chain

More Markov Chain

Markov Chain in C

 

Video Clips (PBS): Lecture Series on Statistics

Normal Distribution I: Stat No 4.wmv (25 min)

Normal Distribution II: Stat No 5.wmv (25 min)

Causality: Stat No 11.wmv (25 min)

Binomial Distribution: Stat No 17.wmv (25 min)

Confidence Intervals: Stat No 19 .mpg (20 min)

Significance Testing: Stat No 20. mpg (20 min)

 

Sp 2011 Assignments

Important: Include below information block at the beginning of each assignment
  Your Full name: Date:  
 

Assignment number and title:

Login dir name for this handin:

Email contact address:

 
 

Names of any partners in assignment:

Login names of any partners:

Email contact addresses of any partners:

 

 

  Assignment Due Date Remarks Handin Dir

#0

 Missing Women.wmv (28 min) 19 Jan (Wed) Large Video Download  
#1

Combinatorics: <doc>   <pdf>

27 Jan (Thu)

Submit your work in .pdf form to  ⇒

 assignment1 :
 Submit a singlefile named login_name.combos.pdf
#2

Expected Value, etc: <doc>   <pdf>

12 Feb (Fri)

Submit your work in .pdf form to  ⇒

 assignment2 :
 Submit a singlefile named login_name.ev.pdf
#3
Normal Distribution:
<docx>   <pdf>

 18 Mar (Fri)

Submit your work in .pdf form to  ⇒

 normal_dist :
Submit a single file named login_name.normal_dist.pdf

#4a
Final Project: Plan 31 Mar (Thu)

Submit your work in .pdf form to  ⇒

 project : Submit a report in pdf named login_name1.login_name2.plan.pdf
#4b

Final Project: Methodology

 12 Apr (Tue)

Submit your work in .pdf form to  ⇒

 approach : Submit a report in pdf named login_name1.login_name2.approach.pdf
#4c

Final Project: Full Implementation and Presentation

Latest :

26 Apr (Tue)

Submit your work in .pdf form to  ⇒

 final_project: Submit a report in pdf named login_name1.login_name2.final_Project.pdf