MAE 127 UCSD

UCSDMAE 127: lecture 18 (Gille) 1 Fitting Data to Sines and Cosines The set of N sines and cosines span all space, which means that if A has N columns and N rows, we can nd an exact t, = b. This is the basis for a Fourier transformwe can represent

Gille-MAE 127 1 Problem Set 4: MAE 127 (Solutions) For this problem set, youll use data collected at three meteorological stations in San Diego County: San Miguel, Alpine, and Mt. Laguna. Download the data from the class website: http:/www-pord.ucs

UCSDMAE 127: lecture 5 (Gille) 1 Figure 1: Observed probability distribution functions of wave height H, wave period T , and wave steepness s for breaking waves (br), nonbreaking waves (nb) and all waves (all), from Holthuusen and Herbers, J. Phys.

UCSDMAE 127: lecture 14 (Gille) 1 Least Squares Fitting and Orthogonality The least-squares t is going to make use of the independent information in each column of A. The columns of A are completely independent if they are orthogonal (or in terms o

UCSDMAE 127: lecture 17 (Gille) 1 Interpreting Mist in a Weighted Fit: Chi-Squared Criteria Once weve compute 2 and compared it with N M , we have to decide if they match well enough. Formally to test whether our least-squares t provides a plausib

UCSD-MAE 127: lecture 10 (Gille) 1 What makes a correlation signficant? Recall from last time that we have defined correlation as: r= (x - x)(y - y) (x - x)2 (y - y)2 How do we tell if a correlation is significantly different from zero? Clearly thi

UCSD-MAE 127: lecture 9 (Gille) 1 no averaging 0.2 0.2 2 point averaging 0.25 4 point averaging 1.5 128 point averaging 0.15 0.15 0.2 1 0.15 pdf pdf pdf 0.1 0.1 0.1 0.5 0.05 0.05 0.05 0 -10 0 x 10 0 -10 0 x 10 0 -10 pdf 0 x 10 0

Gille-MAE 127 1 Problem Set 1: MAE 127 due Friday, April 8, 2005 1. Matlab is going to be an important tool for this course. Get yourself set up to use Matlab either on a UCSD computer or at home. If you don\'t have much Matlab experience, run the M

Gille-MAE 127 1 Final Exam: MAE 127 Tuesday, June 7, 2005 1. Consider data set X in Figure 1. time Figure 1: Sample data distribution. Black areas can be considered to have a numerical value of +1, gray areas 0, and white areas -1. a. Sketch an es