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You'll likely want NonlinearModelFit rather than LogitModelFit as the latter assumes that the response variable has a binomial distribution given the prediction model. Consider the following "logit" model: $y=a/(1+\exp(-k(t-b)) + e$ where $e\sim N(0,\sigma^2)$ and $a$, $b$, $k$, and $\sigma^2$ are parameters to be estimated. That's 4 parameters with only ...


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