This is a graduate level chemistry course on data acquisition and analysis in chemistry. The topics covered in the course include statistical distributions of error, modeling of data, Fourier transform methods, and digital acquisition of data.
Download a pdf of the full syllabus here.
- Statistical Descriptions of Data
- Characterizing Experimental
- Theoretical Distributions
- Confidence Limits
- Hypothesis testing
- Modeling of Data
- Maximum Likelihood
- Linear Models
- Non-Linear Models
- Chi-squared minimization
- The Simplex Method
- The Marquardt Method
- Extracting Confidence Limits for
- Fourier transform techniques
- Fourier Transform pairs
- FT Theorems - Similarity, Addition,
- Digital Fast Fourier
- Multi-channel Spectrometry and the
- Characteristics of analog and digital
- A/D conversion, Sampling
- Signal averaging
- Filtering and smoothing
This course is intended to familiarize graduate students with modern approaches for the acquisition and treatment of information obtained from chemical systems.
, by R. J. Barlow
Data Reduction and Error Analysis for the Physical Sciences, Bevington and Robinson
Numerical Recipes, 2nd Ed., Press, Teukolsky, Vetterling, and Flannery
C, A Programming Language, Kernighan and Ritchie
Statistical Treatment of Experimental Data, Young
There are few prerequisites needed for this course. Undergraduate level calculus and linear algebra should be adequate preparation. Some of the homework will involve writing computer programs.
All Homework must be turned in to be graded. Some of the homework will involve writing computer programs in C. Hard and soft copies of both code and output must be turned in for grading. Computers and compilers are available via ID card access in Room 2105 Newmann-Wolfrom.