GLM Homework

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GLM Homework

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When comparing two linear models the one with a lower AIC is better

2 points

True

False

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If you add a predictor variable to a model the likelihood will always go down.

2 points

True

False

Clear selection

pick the choice that best describes this model: glm(response ~ predictor1 * predictor2, family=binomial)

2 points

response is a function of predictor 1 and predictor 2

response is a function of predictor 1 and predictor 2 as well as the interaction between these

the binary response is a function of predictor 1 and predictor 2 as well as the interaction between these

the Poisson distributed response is a function of predictor 1 and predictor 2 as well as the interaction between these

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pick the answer that best describes what z will have stored in it when you run this code.

2 points

p-value associated with the y intercept being different from zero

p-value associated with the predictor variable x

Betta coef. for x

AIC associated with the model

Clear selection

Thinking of this same code again, how often would you expect x to be a significant predictor of y>

2 points

100% of the time

50% of the time

5% of the time

2.5% of the time

never

Clear selection

Using the chrysina.csv from the course website and one or more linear models determine which tree species (juniper and or oak) are important for the presence of Chrysina gloriosa and C. beyeri. For this question you can ignore random effects and do not need to do transformations.

10 points

both tree species are important for both species of beetles

neither tree species is important for either species of beetle

oak is important for gloriosa and juniper is important for beyeri

juniper is important for gloriosa and oak is important for beyeri

juniper is important for both species

oak is important for both species

oak and juniper are important for gloriosa and oak is important for beyeri

Clear selection

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