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 Distributions
- Theoretical Distributions
- Confidence Limits
- Hypothesis testing
- Modeling of Data
- Maximum Likelihood Estimators
- Linear Models
- Non-Linear Models
- Chi-squared minimization techniques
- The Simplex Method
- The Marquardt Method
- Extracting Confidence Limits for Model Parameters
- Fourier transform techniques
- Fourier Transform pairs
- FT Theorems - Similarity, Addition, Shift, Convolution
- Digital Fast Fourier Transform
- Multi-channel Spectrometry and the Fourier Transform
- Characteristics of analog and digital
- A/D conversion, Sampling theorem
- Signal averaging
- Filtering and smoothing
ObjectivesThis course is intended to familiarize graduate students with modern approaches for the acquisition and treatment of information obtained from chemical systems.
Suggested TextsStatistics, 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
PrerequisitesThere 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.
HomeworkAll 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.