AP Statistics Curriculum 2007 Distrib Binomial

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General Advance-Placement (AP) Statistics Curriculum - Bernoulli and Binomial Random Variables and Experiments

Bernoulli process

A Bernoulli trial is an experiment whose dichotomous outcomes are random (e.g., "success" vs. "failure", "head" vs. "tail", +/-, "yes" vs. "no", etc.) Most common notations of the outcomes of a Bernoulli process are success and failure, even though these outcome labels should not be construed literally.

  • Examples of Bernoulli trials
    • A Coin Toss. We can obverse H="heads", conventionally denoted success, or T="tails" denoted as failure. A fair coin has the probability of success 0.5 by definition.
    • Rolling a Die: Where the outcome space is binarized to "success"={6} and "failure" = {1, 2, 3, 4, 5}.
    • Polls: Choosing a voter at random to ascertain whether that voter will vote "yes" in an upcoming referendum.
  • The Bernoulli random variable (RV): Mathematically, a Bernoulli trial is modeled by a random variable X(outcome) = \begin{cases}0,& s = \texttt{success},\\
1,& s = \texttt{failure}.\end{cases} If p=P(success), then the expected value of X, E[X]=p and the standard deviation of X, SD[X] is \sqrt{p(1-p)}.
  • A Bernoulli process consists of repeatedly performing independent but identical Bernoulli trials.

Binomial Random Variables

Suppose we conduct an experiment observing an n-trial (fixed) Bernoulli process. If we are interested in the RV X={Number of successes in the n trials}, then X is called a Binomial RV and its distribution is called Binomial Distribution.

Examples

  • Roll a standard die ten times. Let X be the number of times {6} turned up. The distribution of the random variable X is a binomial distribution with n = 10 (number of trials) and p = 1/6 (probability of "success={6}). The distribution of X may be explicitely written as (P(X=x) are rounded of, you can compute these exactly by going to [http://socr.ucla.edu/htmls/SOCR_Distributions.html SOCR Distributions and selecting Binomial):
x 0 1 2 3 4 5 6 7 8 9 10
P(X=x) 0.162 0.323 0.291 0.155 0.0543 0.013 0.0022 0.00025 0.000019 8.269e-7 1.654e-8
  • Suppose 10% of the human population carries the green-eye alial. If we choose 1,000 people randomly and let the RV X be the number of green-eyed people in the sample. Then the distribution of X is binomial distribution with n = 1,000 and p = 0.1 (denoted as X \sim B(1,000, 0.1).

Approach

Models & strategies for solving the problem, data understanding & inference.

  • TBD

Model Validation

Checking/affirming underlying assumptions.

  • TBD

Computational Resources: Internet-based SOCR Tools

  • TBD

Examples

Computer simulations and real observed data.

  • TBD

Hands-on activities

Step-by-step practice problems.

  • TBD

References

  • TBD



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