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Teaching material for ST2034: Statistical Modelling for Biologists/Biotechnologists

License: Creative Commons Zero v1.0 Universal

R 0.15% CSS 0.01% HTML 99.85%

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Exercises 9-10: GLMs

Might benefit of a more intuitive explanation why we need a constrained and unconstrained scale: why do we need to model things on the link scale in an unconstrained fashion, and what are the benefits from having it constrained on the response scale. E.g., we want to model the probability of land and this ranges between 0 and 1: connect to that concept.

Update answer sheets

I changed some exercises + they already needed updating. Also check for correct Likelihood notation if applicable.

exercise 2 phrasing

For the lm function the exercise phrases things as if there are x and y arguments, rather than making clear that the function requires a formula input that represents y and x in the regression formula. Many students get confused here.

Module 04 normal

  • Students find the explanation of "pnorm" and "qnorm" confusing, as well as the combination of distributions for the response and for the estimators.
  • at least pnorm-qnorm can be explained easier; pnorm gets p(X<x) while providing x qnorm does the reverse; it gives X while providing p(X<x). Visualising this it's "just" swapping the x-y axes of the plot.

Video for interactions

The course could use a video on interactions, explaining in detail how to calculate different outcomes, because the students seem to struggle with that

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