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We parameterized the model using a binomial response distribution, logit inverse link function, and constant subject-specific random effect (detailed information on the model, estimation procedure, and code are available upon request).
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To further explore seasonality we used a binomial response regression model.
They also expressed their physical and psychological feelings about the beverage using a 5-point scale (1 = not at all to 5 = extremely) and indicated their purchase intent using a binomial (yes/no) response.
Response rates were summarized as a proportion including approximate 90%% confidence intervals (CI) and compared to the null hypothesis response rate using a binomial exact test.
We modelled the binary response variable infection status using a binomial distribution with a logit link function.
Response rates and corresponding 95% CIs were calculated using a binomial distribution.
Then, n1, n2 and n12 responses out of N attempts were generated using a binomial model with probability of success p1, p2 and p12, respectively.
The data were analyzed using a Binomial Generalized Estimating Equations model, which includes the correlation between admission and 5th day responses.
Generalised linear models using a binomial distribution and logit link were used to analyse the stem and foliar pathogen recovery data, which were recorded as binary responses.
Survival was modeled using a binomial error distribution.
Using a binomial test I show strong evidence that there is a preference for solar orientations.
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