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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