AP Statistics 4.7: Random Variables and Probability Distributions
Intro to Random Variables and Probability Distributions
Random variables:: numerical outcomes of random behavior
- ex:
- X = the number of children in a randomly selected household
- W = the time (minutes) it takes a randomly selected person to run a mile
- In each case, the outcome of an individual event is unknown
Discrete random variable:: a random variable that can only take a countable number of values
- ex:
- X = the number of children in a randomly selected household
- Possible values of X include 1, 2, 3, 4, etc…
- It wouldn’t be logical to have 1.2 or 4.5 children
- *countable can mean infinite, but if all the possible values were on a number line, there would be spaces between each value
Continuous random variable:: a random variable that can take an infinite number of values in an interval on a number line
- ex:
- W = the time (minutes) it takes a randomly selected person to run a mile
- Possible values of W include 3.43, 4, 5, 6.8, 7, etc…
- if all the possible values were on a number line, there would be no spaces between each value
Probability distribution:: a display of the entire set of values with their associated probability
- the sum of all probabilities must be zero and each probability must be between 0 and 1, inclusive
Example of a probability distribution:
X | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|
P(X) | 3/23 | 15/23 | 4/23 | 0 | 0 | 0 | 1/23 |
- X = the number of children in a randomly selected household
- P(X) = the probability that a randomly selected household from this sample has X children
- There are 23 households in this sample
Refer to the probability distribution above. Describe each probability in words and solve.
P(1)
- the probability that a randomly selected household has 1 child
- P(1) = 3/23
P(2)
- the probability that a randomly selected household has 2 children
- P(2) = 15/23
P(X < 4)
- the probability that a randomly selected household has less than 4 children
- P(X < 4) = P(3) + P(2) + P(1) = 22/23
Interpreting a Probability Distribution
Use SOCS to describe a probability distribution like any other distribution.
Shape
- How many peaks are there?
- Is it roughly symmetric or skewed?
- On a histogram with X on the x-axis and frequency on the y-axis, the example distribution is unimodal and roughly symmetric
Outliers
- In the example, X=7 is a potential outlier
Center
- Mean (in probability distributions, expected value) commonly used
Spread
- Standard deviation commonly used