Lectures
Lecture 1 Using MatLab
Lecture 2 Looking at Data
Lecture 3Probability and Measurement Error
Lecture 4 Multivariate Distributions
Lecture 5 Linear Models
Lecture 6 The Principle of Least Squares
Lecture 7 Prior Information
Lecture 8 Solving Generalized Least Squares Problems
Lecture 9 Fourier Series
Lecture 10 Complex Fourier Series
Lecture 11 Lessons Learned from the Fourier Transform
Lecture 12 Power Spectral Density
Lecture 13 Filter Theory
Lecture 14 Applications of Filters
Lecture 15 Factor Analysis
Lecture 16 Orthogonal functions
Lecture 17 Covariance and Autocorrelation
Lecture 18 Cross-correlation
Lecture 19 Smoothing, Correlation and Spectra
Lecture 20 Coherence; Tapering and Spectral Analysis
Lecture 21 Interpolation
Lecture 22 Hypothesis testing
Lecture 23 Linear Approximations and Nonlinear Least Squares
Lecture 24 Adaptable Approximations with Neural Networks
Lecture 25 Hypothesis Testing continued; F-Tests
Lecture 26 Confidence Limits of Spectra; Bootstraps
All Lectures