least-squares

I have a fitted 3D data-set using scipy.linalg.lstsq function. I was using: # best-fit quadratic curve A = np.c_[np.ones(data.shape[0]), data[:,:2], np.prod(data[:,:2], axis=1), data[:,:2]**2] C,_,_,_ = scipy.linalg.lstsq(A, data[:,2]) #evaluating on grid Z = np.dot(np.c_[np.ones(XX.shape), XX, YY, XX*YY, XX**2, YY**2], C).reshape(X.shape) But How can I be able to get the R^2 value from this for the fitted-surface .? Is there any way I can check the significance of the fitting result ? Any ideas related to that would be highly appreciated. thank you. etna Following http://en.wikipedia.org/wiki

I am working with data from neuroimaging and because of the large amount of data, I would like to use sparse matrices for my code (scipy.sparse.lil_matrix or csr_matrix). In particular, I will need to compute the pseudo-inverse of my matrix to solve a least-square problem. I have found the method sparse.lsqr, but it is not very efficient. Is there a method to compute the pseudo-inverse of Moore-Penrose (correspondent to pinv for normal matrices). The size of my matrix A is about 600'000x2000 and in every row of the matrix I'll have from 0 up to 4 non zero values. The matrix A size is given by

How would i fit a straight line and a quadratic to the data set below using the leastsq function from scipy.optimize? I know how to use polyfit to do it. But i need to use leastsq function. Here are the x and y data sets: x: 1.0,2.5,3.5,4.0,1.1,1.8,2.2,3.7 y: 6.008,15.722,27.130,33.772,5.257,9.549,11.098,28.828 Can someone help me out please? The leastsq() method finds the set of parameters that minimize the error function ( difference between yExperimental and yFit). I used a tuple to pass the parameters and lambda functions for the linear and quadratic fits. leastsq starts from a first guess

I'm trying to perform a Difference in Differences (with panel data and fixed effects) analysis using Python and Pandas. I have no background in Economics and I'm just trying to filter the data and run the method that I was told to. However, as far as I could learn, I understood that the basic diff-in-diffs model looks like this: I.e., I am dealing with a multivariable model. Here it follows a simple example in R: https://thetarzan.wordpress.com/2011/06/20/differences-in-differences-estimation-in-r-and-stata/ As it can be seen, the regression takes as input one dependent variable and tree sets