Difference between revisions of "CHEM-6111"
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** Statistics: calculating statistical parameters, distributions, precision, uncertainty, ANOVAA | ** Statistics: calculating statistical parameters, distributions, precision, uncertainty, ANOVAA | ||
** Correlation and regressions (variants) | ** Correlation and regressions (variants) | ||
| − | *** vertical vs orthogonal distance | + | *** centered (Pearson's) vs uncentered correlation |
| − | *** squared vs absolute value errors | + | *** vertical vs orthogonal distance regression |
| − | *** weighted vs unweighted | + | *** squared vs absolute value errors for regression |
| + | *** weighted vs unweighted regression | ||
** boxcar and weighted smoothing | ** boxcar and weighted smoothing | ||
** convolution (student request to IMU) | ** convolution (student request to IMU) | ||
Revision as of 15:02, 16 August 2012
Special Topics - Data Analysis and Acquisition - Tentative Outline
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. The course number may be special topics, depending on whether CU can approve the new course number by then.
- Introduction to computer graphing, functions and programming (Igor)
- Data Analysis
- propagation of errors
- Basic analysis: interpolation, area integrals, multidimensional data handling
- Statistics: calculating statistical parameters, distributions, precision, uncertainty, ANOVAA
- Correlation and regressions (variants)
- centered (Pearson's) vs uncentered correlation
- vertical vs orthogonal distance regression
- squared vs absolute value errors for regression
- weighted vs unweighted regression
- boxcar and weighted smoothing
- convolution (student request to IMU)
- Numerical solution of ODEs
- Frequency analysis and FFT
- Montecarlo simulations
- Fitting & Levenberg-Marquardt (as users)
- Principal Component Analysis and Positive Matrix Factorization
- Data analysis problems brought by students from their research
- comparing model data, global datasets (Donna?)
- Data Acquisition
- Fundamentals of data acquisition
- Data I/O, parsing of text files, write to file etc.
- Labview: introduction and programming
- Signal-to-noise, noise reducing measures, signal enhancing measures, numerical high pass filters, low pass filters etc.
- Data acquisition problems brought by students from their research
- Potential Texts
- Numerical Recipes (online)
- Building Scientific Apparatus (Moore, Davis, and Coplan)
- Taylor Error Analysis
- Stuff from Amber's stats class
