4.1 Probability Distribution Function (PDF) for a Discrete Random Variable
Chapter Objectives
Assignment
!! You’re not going to the HW section this time !!
- All vocabulary (see Key Terms for definitions)
- 4 Practice 1–3, 5–8, 10–17
- Or if you prefer, 1–17 and skip 4 and 9
- Solutions
- Read the next section in the book
Probability distribution functions
You might remember function notation from Algebra class, where you had a function like $f(x) = 4x + 5$ and if you plugged in a number for $x$ it would produce an output. For example $f(2)=13$ because $4(2)+5=13$.
Probability distribution functions (or PDFs because that’s too long to type out repeatedly) work the same way, except we are going to only focus on the $f(2)=13$ part. We will completely ignore how we got from $f(2)$ to $13$. Here is an example of that in action.
| $x$ | $P(x)$ |
|---|---|
| $0$ | $\frac{2}{50}$ |
| $1$ | $\frac{11}{50}$ |
| $2$ | $\frac{23}{50}$ |
| $3$ | $\frac{9}{50}$ |
| $4$ | $\frac{4}{50}$ |
| $5$ | $\frac{1}{50}$ |
This is a PDF for for the number of times a newborn baby’s crying wakes a mother after midnight. A sample of 50 mothers was used to collect the data above. We have our input $x$ and our output $P(x)$, which is all we need to do some simple statistical analysis. What is ${P(x=2)}$? That’s just $\frac{23}{50}$. What about ${P(x\le2)}$? That’s $\frac{23}{50} + \frac{11}{50} + \frac{2}{50} = \frac{36}{50}$.
Discrete random variables
We talked about discrete versus continuous way back in 1.2, so I’ll be lazy and copy and paste my slide bullets.
- Discrete quantitative data means only certain numbers, typically whole numbers
- e.g., number of people in a household
- Continuous quantitative data is data where all numbers in a range are valid
- e.g., height, weight, time
The same applies to discrete and continuous random variables. The values are countable for a discrete one, and uncountable for a continuous. The table above is for a discrete variable since we can count the number of times each mother was torn from a peaceful slumber.
When dealing with discrete probability distribution functions, there are two other key points:
- Each probability is between zero and one, inclusive.
- The sum of the probabilities is one.