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  1. B

    Coin toss

    I'm not sure this approach is correct. You cannot say that Y = X^86,381 because toss successions are correlated. If we consider Enuma example and take the particular result HTH then knowing that HT is not 2 heads entails that TH is not two heads. We should introduce some kind of correlation...
  2. B

    Prediction Intervals (Critical Levels)

    Hello, you want to predict X_{n+1} from X_{1}...X_{n} without knowing \mu and \sigma. So, as you said, 1. the estimated variance of \bar{X}_{n} is S_{n}^{2}/n 2. the estimated variance of X_{i} is S_{n} 3. as a result the estimated variance of X_{n + 1} - \bar{X}_{n} is S_{n}^2 (1 + 1/n)...
  3. B

    The mean and variance

    If you know exactly the pdf (probability density function) f(x), the formula for the mean is \mu = E[x] = \int x f(x) dx and for the variance \sigma^{2} = E[(x - E[x])^{2}] = \int (x - E[x])^{2} f(x) dx If you only have experimental data, you can estimate the mean and variance of the...
  4. B

    Chi square problem

    But you do not know the exact frequency of the old technique. So I would suggest to tests : H0 : there is no difference between the two techniques As you observe a large number of events, you can assume gaussian distribution and use a Pearson chi2 test like : chi2 = (250 - 175)^2/(250 +...