Discrete

BA501 : The Class : Stats : Discrete
Discrete Probability Distributions
Introduction

§ Discrete Probability §

Introduction- Lesson 1

Discrete probability distributions share the same characteristics with discrete data. Discrete data are data which have "gaps" between them due to physical or logical reasons. For example, the number of telephone calls made per week is discrete data. It is not physically possible to have 5.4 calls per week. Discrete probability distributions include discrete data and a probability attached to each of the possible data values. One can study general discrete probability distributions and a set of special distributions. The special distributions include the binomial, hypergeometric and Poisson. For our purposes, you will study the general nature of probability, general discrete probability distributions and the Poisson.

§ Probability §

§ General Discrete Probability Distributions §

§ Poisson Distribution §

Probability

The probability of an event (outcome) of an experiment occurring can be viewed as a relative frequency. Probability may also be called the chance, odds, percentage and proportion. An experiment occurs when the outcomes of it are uncertain. One might know the possible outcomes (a head or tail on the single flip of a coin) of an experiment, but the outcome is uncertain until the experiment is actually performed. See Statistics-Measures-Equations to review the calculation of a relative frequency. (click me)

The number of times an event has occurred historically in a population is a good basis for estimating the probability of the event. True False (click one)

The probability of an event occurring is one if the outcome is ____ uncertain.

General Discrete Probability Distributions

A general discrete probability distribution is one that can not be identified as a special discrete probability distribution. The special discrete probability distributions include the binomial, hypergeometric and the Poisson. A general discrete probability distribution usually consist of a set of discrete data and the results of a given probability mass (density) function. A probability mass function results (individual probabilities) come either from an equation which generates the discrete probabilities or from the inspection of population data to determine the proportion of the time outcomes occur. (click me)

A general discrete probability distribution measures the probability of the number of occurrences of an event over a period of time. True False (click one)

The probability of _________ values of x occurring are identified in a general discrete probability distribution.

Poisson Distribution

The Poisson distribution measures the probability events occurring over a given time period. For example, one might be interested in the probability of a certain number of cars arriving at a drive in bank teller during the various office hours of the bank. The individual probabilities can be calculated with a probability mass function for this special discrete distribution, but you will use a Poisson table to find the probabilities. (click me).

The Poisson distribution uses continuous data. True False (click one)

The Poisson distribution is _____-_____ when plotted as a graph.

You should now:

Go on to Lesson 2: Examples

Go back to Discrete Distributions: Assignments and Activities

Please reference "BA501 (your last name) Assignment name and number" in the subject line of either below.

E-mail Dr. James V. Pinto at BA501@mail.cba.nau.edu
or call (928) 523-7356. Use WebMail for attachments.