Stats 4.2Concept6 parts

Bernoulli and Binomial Outcomes

Model one yes/no outcome and the number of successes in a group.

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In this lesson6 parts

  1. 01Turn a recorded fraction into a stated model input
  2. 02One yes-or-no outcome is Bernoulli
  3. 03Ten trials become one binomial count
  4. 04Increase the cohort size, then derive mean and spread
  5. 05Check the model before using its count range
  6. 06Reproduce and challenge the conditional result

Key terms

The words this lesson introduces, each in one line. The module’s glossary collects them all.

churn indicator
One for a recorded churn label, zero otherwise.
parameter p
The assumed chance of outcome one on one modelled trial; here set to 15.71%.
Bernoulli trial
One modelled outcome that is one with probability p and zero otherwise.
expected value
Probability-weighted average across possible outcomes.
variance
Probability-weighted squared distance from the model mean; p(1 − p) for one Bernoulli trial.
binomial count
Number of ones across a fixed number of Bernoulli trials.
independent
One trial's outcome does not alter another's probability.
identically distributed
Every trial uses the same outcome probabilities.
binomial distribution
Probability pattern of the count across a fixed number of independent Bernoulli trials.
binomial mean
n times the one-trial probability p.
standard deviation
Square root of variance, in the count's units: √(n p (1 − p)).

Quiz 5 questions

Your first pick on each question is the one that counts, and a right one earns a coin. Getting one wrong here is how the lesson sticks.

Practice

Problems to solve in your own notebook. Each states the problem, not the steps: working out the steps is the exercise. Level A applies the lesson, B combines it with earlier ones, C stretches it.

The self-checking notebook for this lesson is Random sampling and count models.

Common mistakes

What you will see when it goes wrong, why it happens, and the fix.

Where it’s used

Where this lesson’s ideas turn up in real work.