Here are four examples of univariate Poisson distributions, varying on their values of p The first Poisson distribution has u=0.8 The second. F=1.5. The third, F=2.9 The fourth F =10.5 The Stata commands to produce the Four Univariate Poisson Distributions: 0.8, 1.5. 2.9 and 10.5 A critical assumption of the Poisson distribution is that when an event occurs. it prmoumls pya, plot max 20) does not affect the probability of the event occurring in the future. If the "count is tombs pyb, plot max(20) children born to mothers the assumption of independence implies that when a woman has a baby born to her, it does not affect the probability of another baby being born to her prmoumts pyd, plot max(20) In demography, however, future fertility is not independent from past fertility, and rticularly in China, the next birth( abortion)is heavily dependent upon the graph, pyapreq pytpreq pyrpreq pydpreq pyaval, dlm) gap(3) n,"probabality") previous ones in the context of the strict family planning policy 87 13 • Here are four examples of univariate Poisson distributions, varying on their values of μ. • The first Poisson distribution has μ =0.8. The second, μ =1.5. The third, μ =2.9. The fourth, μ =10.5. • The Stata commands to produce the graph are as below: 14 8 15 16 • A critical assumption of the Poisson distribution is that when an event occurs, it does not affect the probability of the event occurring in the future. If the “count” is children born to mothers, the assumption of independence implies that when a woman has a baby born to her, it does not affect the probability of another baby being born to her. In demography, however, future fertility is not independent from past fertility, and particularly in China, the next birth (or abortion) is heavily dependent upon the previous ones in the context of the strict family planning policy