Chapter 6 Probability Density Functions
In the previous chapter, we modeled data with theoretical distributions including the binomial, Poisson, exponential, and normal distributions.
The binomial and Poisson distributions are discrete, which means that the outcomes have to be distinct or separate elements, like an integer number of hits and misses, or goals scored. In a discrete distribution, each outcome is associated with a probability mass.
The exponential and normal distribution are continuous, which means the outcomes can be at any point in a range of possible values. In a continuous distribution, each outcome is associated with a probability density. Probability density is an abstract idea, and many people find it difficult at first, but we’ll take it one step at a time. As a first step, let’s think again about comparing distributions.
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats/raw/v3/nb/thinkstats.py")
try:
import empiricaldist
except ImportError:
%pip install empiricaldist
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from thinkstats import decorate
