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Question

# species region pos_x pos_y age long_cm wide_cm sex lesions injured teeth_condition weight antibody pollutant

The dataset Marmots_real.csv has the data from 60 marmots, including many variables that are easier to measure:

Variable Name Type Description

Species Categorical, Unordered One of five species of giant marmot

Region Categorical, Unordered One of five regions around Moscow where the subject is captured

Age Numerical, Continuous Age in years

Pos_x Numerical, Continuous Longitude, recoded to (0,1000), of capture

Pos_y Numerical, Continuous Latitude, recoded to (0,1000), of capture

Long_cm Numerical, Continuous Length nose to tail in cm

Wide_cm Numerical, Continuous Width between front paws, outstretched

Sex Binary M or F

Lesions Numerical, Count Number of skin lesions (cuts, open sores) found upon capture

Injured Binary 0 or 1, 1 if substantial injury was observed upon capture.

Teeth_Condition Categorical, Ordered Condition of teeth upon capture, listed as Very Bad, Bad, Average, or Good.

Weight Numerical, Continuous Mass of subject in 100g

Antibody Numerical, Continuous Count of CD4 antibody in blood per mL

Pollutant Numerical, Continuous mg/kg of selenium found in bone marrow

There are no sampling weights. There is no missing data. There should be little to no convergence or computational issues with this data.

Assignment parts:

M1) Build a logistic model of injured, as a function of species, age, weight, number of lesions, and amount of pollutant.

a) Write the regression equation. (do not include the error term)

b) Interpret the intercept

c) Interpret the first of the species coefficients.

d) Interpret the age coefficient.

M2) Make a model with a better AIC than the one shown in the example code. Show this by using the AIC() function on both the original model and your model.

Useful sample code:

#### MARMOTS

######## Preamble / Setup

## Load the .csv file into R. Store it as 'dat'

dat\$region = as.factor(dat\$region)

library(car) # for vif()

### Build a logistic model of injured or not.

mod = glm(injured ~ species + age + weight + lesions + pollutant, data=dat, family="binomial")

summary(mod)

vif(mod)

plot(mod)

AIC(mod)

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