Hang out with the TAs from STA 210! This is a casual conversation and a fun opportunity to meet the members of the STA 210 teaching team. The only rule is these can’t turn into office hours!
Tea with a TA counts as a statistics experience.
Team Feedback #1 due Wed, Oct 7 at 11:59p. You will receive an email from Teammates with a link to the feedback from. Email Professor Tackett if you did not receive the email (check your spam folder first!). Team feedback is part of the participation grade.
Project proposal due Fri, Oct 9 at 11:59p.
This dataset is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. We want to use the total cholesterol to predict if a randomly selected adult has a high risk of having coronary heart disease in the next 10 years.
high_risk
:
totChol
: total cholesterol (mg/dL)heart <- read_csv("data/framingham.csv") %>%
select(totChol, TenYearCHD) %>%
filter(!is.na(totChol)) %>% #remove observations with missing cholesterol
mutate(high_risk = as.factor(TenYearCHD))
What is the probability a randomly selected person in the study does not have a high risk of heart disease?
What are the odds a randomly selected person in the study does not have a high risk of heart disease?
Fit the appropriate model to predict whether a person has a high risk of heart disease given their total cholesterol.
Based on the model, if a randomly selected person has a total cholesterol of 225,
Based on the model, if a randomly selected person has a total cholesterol of 226,
Based on your answers in the previous section, how do the log-odds change when going from a person with total cholesterol of 225 mg/dL to a person with total cholesterol of 226 mg/dL?
How would you interpret the coefficient of totlChol
in terms of the log-odds?
What would be the interpretation of totChol
in terms of the odds?