# Configure Jupyter so figures appear in the notebook %matplotlib inline # Configure Jupyter to display the assigned value after an assignment %config InteractiveShell.ast_node_interactivity='last_expr_or_assign' # import classes from thinkbayes2 from thinkbayes2 import Hist, Pmf, Suite
According to the CDC, "Compared to nonsmokers, men who smoke are about 23 times more likely to develop lung cancer and women who smoke are about 13 times more likely.''
Also, among adults in the U.S. in 2014:
Nearly 19 of every 100 adult men (18.8%) Nearly 15 of every 100 adult women (14.8%)
Exercise: If you learn that a woman has been diagnosed with lung cancer, and you know nothing else about her, what is the probability that she is a smoker?
# Solution # In this case, we can't compute the likelihoods individually; # we only know the ratio of one to the other. But that's enough. # Two ways to proceed: we could include a variable in the computation, # and we would see it drop out. # Or we can use "unnormalized likelihoods", for want of a better term. # Here's my solution. pmf = Pmf(dict(smoker=15, nonsmoker=85)) pmf['smoker'] *= 13 pmf['nonsmoker'] *= 1 pmf.Normalize() pmf.Print()
nonsmoker 0.30357142857142855 smoker 0.6964285714285714