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These components were then used as independent variables in a logistic regression model (fit using fitglm in matlab with a binomial distribution) to predict the outcome of the upcoming trial.
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Similarly, we used a generalized linear model with a negative-binomial distribution to predict tick abundance at a site from the PCA axes with which tick abundance was significantly correlated.
We used an identical model for all classes, which was fitted using a negative binomial distribution to take into account overdispersion.
For many other efficient codes, we have to fit the parameters of a model (e.g., the binomial distribution) to the distribution of gaps in the index.
We can use the binomial distribution to solve the problem.
Frequency distribution was compared to binomial distribution to evidence the phase separation and atom clustering.
We have devoted two lines in Table 9 to the binomial distribution to illustrate its inaccuracy.
It uses a negative binomial distribution to model total variation.
We used normal approximation of binomial distribution to construct confidence intervals (CI) for each indicator.
Finally, we used a negative binomial distribution to estimate the statistical significance of the peaks.
3) Add the capability to use both Gaussian and Binomial distributions to represent the component distributions.
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