Difference between revisions of "CHEM-6111"
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*** weighted vs unweighted regression | *** weighted vs unweighted regression | ||
** boxcar and weighted smoothing | ** boxcar and weighted smoothing | ||
| + | ** Signal-to-noise, noise reducing measures, signal enhancing measures, numerical high pass filters, low pass filters etc. | ||
** Custom fitting of data | ** Custom fitting of data | ||
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** Data I/O, parsing of text files, write to file etc. | ** Data I/O, parsing of text files, write to file etc. | ||
** Labview: introduction and programming | ** Labview: introduction and programming | ||
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** Data acquisition problems brought by students from their research | ** Data acquisition problems brought by students from their research | ||
Revision as of 14:20, 17 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, 2 weeks)
- Basic Data Analysis (Igor, 6 weeks)
- 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
- Signal-to-noise, noise reducing measures, signal enhancing measures, numerical high pass filters, low pass filters etc.
- Custom fitting of data
- Advanced Data Analysis (Igor, 4 weeks)
- convolution (student request to IMU)
- Numerical solution of ODEs
- Frequency analysis and FFT
- Montecarlo simulations
- Principal Component Analysis and Positive Matrix Factorization
- Data Acquisition (Labview, 3 weeks)
- Fundamentals of data acquisition
- Data I/O, parsing of text files, write to file etc.
- Labview: introduction and programming
- 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
