ST214: Mathematical Analysis of Experimental Data

Term: August 

Credits: 3:0  

For graduate students only 

This course is aimed at graduate students working in data analysis. As outlined in the course content, it exposes the student to collecting and analyzing data. It also bridges the gap between physics and mathematics giving due importance to both. 

Prerequisites: None 

Syllabus 

Design of Experiments, Data types, and data gathering tools. Errors, systematic & random errors, methods to minimize them, and account for them. Measurement variability. Instrument calibration and corrections at different scales. Significant figures. Uncertainty analysis and curve fitting; Data analysis of data distribution, normal, and t-distribution, confidence interval and hypothesis testing. Design of experiments: replication, randomization, blocking and controls. ANOVA, single factor experiments, randomized blocks, Latin square designs, factorial and fractional factorial designs. Simple and multiple linear regressions. Mathematical analysis of experimental data from problems in fluid flow, heat transfer, and combustion.