Modeling Random Events: The Normal and Binomial Models

These flashcards cover key vocabulary and concepts related to probability distributions, specifically focusing on the Normal and Binomial models in statistics.

Probability Distribution

A description of how we think data from a random scenario is generated; it tells the possible outcomes and their probabilities.

Normal Model

A probability model describing many real-life continuous variables, represented by a bell-shaped curve.

Binomial Model

A probability model that describes the number of successes in a fixed number of independent trials.

Discrete Outcomes

Numerical values that can be listed or counted.

Continuous Outcomes/Continuous Variables

Numerical values that occur over a range and cannot be counted.

Empirical Rule

A guideline for the distribution of data; 68% of data within 1 standard deviation, 95% within 2 standard deviations, and nearly all within 3.

Probability Mass Function (pmf)

A table or graph (or sometimes formula) of a discrete probability distribution.

Z-score

A measurement of how many standard deviations an element is from the mean.

Standard Normal Distribution

The normal distribution with a mean of 0 and a standard deviation of 1, denoted as N(0, 1).

Binomial Coefficient

The number of ways to choose x successes from n trials, denoted as (n choose x) or nCk.