CHEM-6111

Special Topics: Data Analysis and Acquisition

This 3-credit course will be offered by Dr. Ingrid Ulbrich (with assistance from Prof. Jose L. Jimenez) in Fall of 2012. It will be a required course for 1st year analytical chemistry students, and optional for others.

Course Information

• Lectures: Tue & Thur 12:30 pm - 1:45 pm, Ekeley W166
• Office Hours: TBD
• Course Syllabus
• Required Texts
  • Taylor, Error Analysis, 2nd. Ed., 1997. ISBN: 093570275X, on reserve at Norlin
  • Press, Teukolsky, Vetterling, and Flannery, Numerical Recipes
    • 2nd. Ed. free online You can read the version for any of the coding languages; we'll focus on the discussion, not the code.
    • 3rd. Ed. available for purchase online or book, 2007. ISBN: 9780521884075, on reserve at Norlin.
  • Allen and Tildesley, Computer Simulation of Liquids, 1987. Selections will be posted here.

Tentative Assignment Dates

All assignments are due at the start of class. All homeworks will include electronic submission of an Igor experiment file, and some may also include submission of written material in class.

• HW 1: Tuesday, 9/11/2012
• HW 2: Thursday, 9/27/2012
• HW 3: Tuesday, 10/16/2012
• Midterm Exam: 10/18/2012 (in class), on all of the "basic" techniques below
• HW 4: Tuesday, 11/6/2012
• HW 5: Thursday, 11/29/2012
• HW 6: Friday, 12/14/2012
• Final Exam: 12/19/2012, 1:30-4 pm, cumulative, with emphasis on the latter half of the course; location TBD

Introduction to computer graphing, functions and programming (Igor, 2 weeks)

• Igor Resources
  • Igor_Quick_Reference. A page in the wiki with most of the the commands that are introduced in Getting Started, but aren't easy to find there.
  • Online version of the Igor User's Manual that also has a link to the pdf version.
    • pdf version also available inside Igor under Help

• Homework Igor Resources
  • Igor Homework Template
    • Please use this for all of your homeworks! Open it when you start each homework. When you're ready to save your experiment, it will ask you to save in the regular experiment format, .pxp (packed experiment). Do save it that way. Don't overwrite the template file!
  • Igor Programming Conventions
    • Save this file, then open it on your computer. It will load into Igor (.ipf is Igor Procedure File).

• Lecture Notes: Course Intro
  • Work through "Getting Started" in the Igor Manual
• Lecture Notes: Simple Function

• Homework 1, due Thursday, Sept. 6 at the beginning of class, in the d2l dropbox.

Basic Data Analysis (Igor, 6 weeks)

• Reading for Tues, 9/4/12: Taylor Ch. 1-4 (emphasis on 3 & 4)
• precision, uncertainty
  • Taylor Ch. 2
• propagation of errors (Taylor Ch. 3)
  • Lecture Notes: Error Propagation Function
  • Lecture Notes: Error Propagation Function with for loop
• Statistics:
  • statistical parameters
    • Avg, Std Dev, Pop Std Dev, SDOM
      • Taylor Ch. 4
      • Example Data
      • NR Ch. 14
• Homework 2, due Thursday, Sept. 13 at the beginning of class, in the d2l dropbox + paper
• Lecture Notes: Good graphs and Igor Files
• Online sampling distribution tool
• Lecture Notes: Sampling Statistics and waves in Igor
• Histograms & number of bins
  • Igor manual
  • Taylor Ch. 5
• Distributions
  • Gaussian (normal): Taylor Ch. 5
  • Binomial: Taylor Ch. 10
  • Poisson: Taylor Ch. 11
    • Derivation of the Poisson distribution from the binomial distribution: Kahn Academy - Part 1, Kahn Academy - Part 2
  • Lecture Notes: Normal and Binomial Distributions
  • Lecture Notes: Binomial & Poisson Distributions
  • Lecture Notes: more Poisson Distributions
  • Lecture Notes: Hypothesis Testing
• Homework 3, due Tuesday, Sept. 27 at the beginning of class, in the d2l dropbox
  • Exam score data itx
• ANOVA
  • Zar, Bioanalytical Statistics
  • Lecture Notes: ANOVA
  • Lecture Notes: Histogram Code Review
• Simple Linear Fits
  • vertical vs orthogonal distance regression
  • squared vs absolute value errors for regression
  • weighted vs unweighted regression
  • Lecture Notes: Linear Fits
• Homework 4, due Thursday, Oct. 18 at the beginning of class, in the d2l dropbox
  • Lecture Notes: Midterm Exam Review

• • Stat parameters NR Ch. 14
• Fitting of custom functions
  • fitting known spectra to total spectrum
  • cubic spline fits
  • fixed x wave vs. free x wave
• interpolation,
• area integrals
  • NR Ch. 4
  • between markers
  • multidimensional data handling
• Correlation and regressions (variants)
  • • Taylor Ch. 9
    • NR Ch. 13.2, 14.5
  • centered (Pearson's) vs uncentered correlation
    • NR Ch. 15.0-3
• boxcar and weighted smoothing
  • Allen Ch. 6.5, NR Ch. 14.9
  • propagation of errors
• Signal-to-noise, noise reducing measures, signal enhancing measures, numerical high pass filters (freq response in smoothing), low pass filters etc.
• Fitting of simple functions
  • Taylor Ch. 8 (least squares)
  • chi2 Taylor Ch. 12

Advanced Data Analysis (Igor, 4 weeks)

• Numerical solution of ODEs
  • Euler's Method
  • Pre-Lecture Notes: Euler's Method
  • NR Ch. 17 (Runge-Kutta) (NR 3rd ed. Ch. 17; 2nd ed. Ch 16; 1st ed. Ch 15)
• convolution
  • NR Ch. 13.1
• Montecarlo simulations
  • Allen Ch. 4, 7.3
  • NR Ch. 7.3, 7,7, 7.9, 15.8
• Positive Matrix Factorization
• Frequency analysis and FFT
  • Allen Ch. 6.3, 6.5
  • NR Ch. 12, 13

Data Acquisition (Labview, 3 weeks)

• Fundamentals of data acquisition
• Data I/O, parsing of text files, write to file etc.
• Labview: introduction and programming

Additional Resources

• Additional Texts
  • Building Scientific Apparatus (Moore, Davis, and Coplan)