Course curriculum

  • 1

    Theory of Curve Fitting (free content)

  • 2

    Linear Regression in MATLAB

    • Example 1 -- Linear Regression

  • 3

    Nonlinear Regression in MATLAB

    • Example 1 -- Fit to a Gaussian

  • 4

    Polynomial Fitting in MATLAB

    • Example 1 -- Fit to a Quadratic

Pricing options

A one time fee of $14 gives you a year's worth of access to the instructional videos


Founder of EMPossible and Professor at Univ. of Texas at El Paso

Dr. Raymond Rumpf

Dr. Raymond (Tipper) Rumpf is the EMProfessor, world renowned research and educator in the fields of computation and electromagnetics. He is the Schellenger Professor of Electrical Research in the Department of Electrical & Computer Engineering at the University of Texas at El Paso (UTEP) and the Director of the EM Lab. Dr. Rumpf formed the EM Lab with a mission to develop revolutionary technologies in electromagnetics and photonics. Under Dr. Rumpf’s leadership, the EM Lab has produced numerous breakthroughs, discoveries, and first-ever achievements. Raymond earned his BS and MS in Electrical Engineering from the Florida Institute of Technology in 1995 and 1997 respectively. He earned his PhD in Optics in 2006 from the University of Central Florida. Raymond has been awarded many research, mentoring, and teaching awards including the 2019 Dean’s Award for Excellence in Research, Most Outstanding Faculty Member in 2016/2017, and the highly prestigious University of Texas Regents’ Outstanding Teaching Award. Raymond holds five world records for skydiving and has been awarded more than a dozen United States patents. He is an Associate Editor for SPIE Optical Engineering, a Fellow of SPIE, and a Senior Member of IEEE. He is also a member of OSA, and ARRL. Raymond is active in outreach with local grade schools in El Paso as well as helping students in third-world countries.

Don’t struggle - Learn it here!

Learn the algorithms for fitting measured data to curves. Learn MATLAB and visualizing data associated with these methods. Methods include linear regression, nonlinear regression, and fitting polynomials.