This week we finally made it to the culmination of a lot of our work this semester. In this document I go over the syntax of the glmm R package with the salamander data set.

This week we reviewed some LMM R techniques and analysis. This RPub is a great example of how to properly include random effects into lmer models.

Since we did not have class this week, I used this Rpub as a review for all the model types and equation structures we have gone through in this class.

This week we went through some examples of how to code and analyze linear mixed models in R. Note: I apologize if this is on my page twice. I published this last night but now I don't see it on my page.

This week we continued learning about random effects and how to deal with correlated data. We have not done any rigorous R coding for this yet so this Rpub will serve as a reference for determining whether to use nested or crossed random effects.

Prior to spring break we discussed the basics of correlated data with Dr. McNamara. This Rpub will serve mostly as a general overview of what we discussed in class.

This week was more work on logistic regression and interpreting log odds, odds, probabilities, and odds ratios.

This week we were introduced to logistic regression. I covered what we went through in class and also included some probability review.

This week we worked on our Poisson Regression assignment. We did not discuss any new material so this is mostly a review of some Poisson output interpretation that wasn't in last week's Rpub document.

Encompasses a lot of aspects of Poisson Regression technique and interpretation.

This week we started on Poisson regression and how to interpret some of the R output.

This week we reviewed model comparison for the most part so, in this Markdown, I reviewed LRT, Wald's Test, T-test, AIC, and BIC with an actual dataset and examples

Problem #2 and #3 of the homework. Goes over graphing log likelihood and model comparison techniques/criteria

This week we did a review on interpreting linear model results and different ways to compare and determine which models are better.