Univariate Distributions

  • Teacher instructions to accompany "Markov vs. Markov" case study found at http://ublib.buffalo.edu/libraries/projects/cases/markov/markov.html.
    0
    No votes yet
  • This applet allows you to manipulate the starting population, age-class survival rates, and age-class fecundity rates over 10 generations for up to 6 age classes. The default gives you the same population size for each age class as well as the same fecundity rate and survival rates. Move the sliders for each age class to manipulate each of these factors. You will see the relative proportions of each age class will change over time, but will eventually reach a stable age distribution.
    0
    No votes yet
  • This page will generate a graphic and numerical display of the properties of a binomial sampling distribution, for any values of p and q, and for values of n between 1 and 40, inclusive.

    0
    No votes yet
  • For a situation in which independent binomial events are randomly sampled in sequence, this page will calculate (a) the probability that you will end up with exactly k instances of the outcome in question, with the final (kth) instance occurring on trial N; and (b) the probability that you will have to sample at least N events before finding the kth instance of the outcome.

    0
    No votes yet
  • This page calculates the Poisson distribution that most closely fits an observed frequency distribution, as determined by the method of least squares. Users enter observed frequencies, and the page returns the fitted Poisson frequencies, the mean and variance of the observed distribution and the fitted Poisson distribution, and R-squared.

    0
    No votes yet
  • This lesson on the Poisson distribution explains the theory, history, and applications of the distribution and gives examples and a multiple choice test.
    0
    No votes yet
  • This page will perform the procedure for up to k=12 sample values of r, with a minimum of k=2. It will also perform a chi-square test for the homogeneity of the k values of r, with df=k-1. The several values of r can be regarded as coming from the same population only if the observed chi-square value proves the be non-significant.

    0
    No votes yet
  • Using the Fisher r-to-z transformation, this page will calculate a value of z that can be applied to assess the significance of the difference between r, the correlation observed within a sample of size n and rho, the correlation hypothesized to exist within the population of bivariate values from which the sample is randomly drawn. If r is greater than rho, the resulting value of z will have a positive sign; if r is smaller than rho, the sign of z will be negative.

    0
    No votes yet
  • To assess the significance of any particular instance of r, enter the values of N[>6] and r into the designated cells, then click the 'Calculate' button. Application of this formula to any particular observed sample value of r will accordingly test the null hypothesis that the observed value comes from a population in which rho=0.

    0
    No votes yet
  • This calculator returns the value of t for the difference between the means of two correlated samples, for sample sizes up to 10. Users are prompted for sample size as the page opens. It will also calculate various summary statistics for the two samples.

    0
    No votes yet

Pages

register