Correcting for over-dispersion when using Pearson chi-squared

In this past module, we discussed the various merits and applicability of the Least Squares, Pearson chi-square, Poisson likelihood, and Negative Binomial likelihood statistics.

And in this past module we discussed how we can use the graphical Monte Carlo method (aka fmin plus a half method) to determine the one-std deviation confidence interval on our parameter hypotheses when using a likelihood statistic, and we also discussed how the Least Squares and Pearson chi-square statistics can be converted to likelihood statistics.

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Example using Negative Binomial likelihood for model parameter optimization

In this past module, we discussed using the Pearson chi-squared statistic to determine the best-fit parameters of an SIR model to influenza B data from the 2007-08 Midwest flu season.   In this module, we will discuss how to find the best-fit parameters using the Negative Binomial likelihood instead.

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