Assignment 11 (2019) – interactions

Put all your answers in a word document

Generally, while we should always report the betas and C.I. from our analyses on main effects, I believe that if interactions are non-significant, it is enough to say so (with the p-value) and take the interaction OUT of the model before extracting betas from main effects. The reason is because interactions make everything so much more complicated.

Alternatively, if interactions are significant, then I recommend breaking up your analyses if possible. Analyze one group of data from a chosen categorical variable separately from the other group(s) of data.

Truth for all data

Case 1 - Data

In this example, you have a continuous x-variable (e.g., mast crop - the estimated amount of tree seeds produced in a given year [kg/ha]) and one categorical x-variable (pine forest vs mixed hardwood forest) and you are looking at their effect on squirrel density (squirrels / hectare). In this system, there is an interaction between mast crop and type of forest. Write example sentences that you might include in a manuscript for publication that describes the observed results. Be sure to explain that there is an interaction with the appropriate p-value.

Case 2 - Data

In this example you have a categorical x-variable (QDM (Yes) vs. non-QDM (No), where 'QDM' stands for 'Quality Deer Management' which is prescription for managing deer in a way that supposedly improves deer quality) and another categorical x-variable (sex), and you are interested in the relationship between those variables and adult body mass (kg) in deer. Write example sentences that you might include in a manuscript for publication that describes the observed results. Be sure to explain that there is an interaction with the appropriate p-value.