On Thu, Sep 9, 2010 at 7:22 PM, cpblpublic <[email protected]> wrote: > I am looking for some reaally basic statistical tools. I have some > sample data, some sample weights for those measurements, and I want to > calculate a mean and a standard error of the mean.
How about using a bootstrap? Array and weights: >> a = np.arange(100) >> w = np.random.rand(100) >> w = w / w.sum() Initialize: >> n = 1000 >> ma = np.zeros(n) Save mean of each bootstrap sample: >> for i in range(n): ....: idx = np.random.randint(0, 100, 100) ....: ma[i] = np.dot(a[idx], w[idx]) ....: ....: Error in mean: >> ma.std() 3.854023384833674 Sanity check: >> np.dot(w, a) 49.231127299096954 >> ma.mean() 49.111478821225127 Hmm...should w[idx] be renormalized to sum to one in each bootstrap sample? _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
