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Start with plotting your data. It seems you would like a separate analysis for each time point? You are right that this is not paired data, so you should not use the paired t-test, use the independent …

Since there is no pairing, and no reason to believe the 20 fish weighted at both time points are the same, some independent groups analysis should be used. You have two time points, and two groups, …

If the cells have different infectivity, with a distribution $g$ say over infectivity, the resulting distribution of number of viral genomes (if I am understanding the situation correctly ...) will be …

As year has only two distinct values, there is nothing to gain by representing it as a random effect, see What is the minimum recommended number of groups for a random effects factor?. That leaves you …

You need to start with something on the applied side, like Frank Harrell: "Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer Seri …

It is your first equation. When you think about what RR (relative risk) means, it is the risk in the treatment group compared to the risk (probability of being affected) for the control group, where …

It would have been helpful if you included some more information such as sample sizes and maybe some plots. But, assuming your sample sizes are adequate, you can rank the observations (there will be t …

It seems like the distribution fitting you have done are on the marginal distribution of the response. But what you need is the conditional distribution given the predictor variables, see for instance …

Adding to the other answers with some experimental calculations. The large standard error for managementD is caused by small sample size. The standard error you've got is based on an approximation, ba …

Your dataset is quite large (total n = 4382), so there might be other issues, like dependence ... but concentrating on your data, first, present is as an contingency table : ExistenceOf …

No, you should not "unnormalize"! When body weight is already in your model, including other (metabolic) variables as a total, will, in a sense, represent body weight multiple times in the model (and …

The usual way of constructing contingency tables is to let one factor be in rows, and the other in columns. That corresponds to your table 2, which is what you should use for your analysis, and yes, y …

Your variable group is a factor with three levels, control, Fertilizer_A, Fertilizer_B, and control is used as reference )or baseline) level, so its implied coefficient is zero. See What to do in a mu …

It seems you are treating days (within treatments) as blocks. There might well be reasons for doing that, even if you don't told us them. So just let the data decide if there really is block differenc …

You should use a Poisson regression (or in case of overdispersion, maybe negative binomial or quasi-Poisson regression). There is no assumption of normality in those models, so no need for transformat …