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You have a data from 100 stations worldwide that measured daily CO2 in the atmosphere over...

You have a data from 100 stations worldwide that measured daily CO2 in the atmosphere over a year (365 days in total). You are interested to find out what are the most characteristic temporal signals of CO2 over a year, i.e. those that carry most of the variance. So you apply PCA (Principle Component Analysis) on the data. a) What will be the dimension of the resulting eigenvectors (ie. how many elements per eigenvector) and what will be the dimension of PCs (i.e. how many elements per PC)? b) What dimension are you compressing in this case: 'number of stations' or 'number of days'? c) How would you know which of the 100 stations show a signal that is the most similar to the signal of the first PCA mode)?

math   Statistics-And-Probability   
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