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ECON 2000 UU Variety of Models to Predict if A Person Got Vaxx Program
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wnzobb
Computer Science
ECON 2000
umbrella university
ECON
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README.md
Lab 7
Econ B2000, MA Econometrics
Fall 2021
For this lab, we will estimate a variety of models to try to predict if a person got vaxx (same data as
last week). Compare logit with OLS in terms of prediction and set up the variables to be ready to
expand into other models (next week). And give me some better output, it’s time to stop dumping all
your output into one file but instead get thoughtful about presenting results.
First decide on how you’re defining your subgroup (all adults or 12+? Within certain age? Other?)
then find some basic statistics – what fraction are not vaxxed? (Later go back to look at simple stats
for subgroups to see if there are sharp differences.) Explain what you’re doing with NA. You did this
last week (along with defining vaxx) so check back. You might do the same or choose to improve.
Run several different types of models to explain vaccination rates with some explanatory
variables, vaxx ~ TBIRTH_YEAR + EEDUC + MS + RRACE + RHISPANIC + GENID_DESCRIBE +
REGION. Compare the confusion matrix for linear model and logit. Look at subgroups to see if there
are particular groups where the models are more confused. Look at the tradeoff of false positive vs
false negative. Are there explanatory variables (features) that are consistently of little predictive
value? Can you find better ones?
Are these X-variables exogenous? As you add more, think about causality.
We want to set up the data in a way that is common to all of the models.
Some of the estimation procedures are not as tolerant about factors so we need to set those as
dummies. Some are also intolerant of NA values. I’ll show the code for the basic set of explanatory
variables, which you can modify as you see fit.
The R command model.matrix() creates a set of dummy variables out of a factor. Run this to see a
for-instance:
d_educ Purchase answer to see full
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