Discrete and Continuous Probability
All probability distributions can be categorized as discrete probability distributions or as continuous probability distributions (stattrek.com). A random variable is represented by “x” and it is the result of the discrete or continuous probability. A discrete probability is a random variable that can either be a finite or infinite of countable numbers. For example, the number of people who are online at the same time taking a statistics class at CTU on a given day is a discrete random probability. Another example of a discrete random probability is the number of people who stand in a checkout lane in Kroger on a given day. A continuous probability is a random variable that is infinite and the number is uncountable. An example of a continuous probability is the wait time in a Kroger line on a given day and time and that number could be 5 minutes, 5.2 minutes, or 5.34968...minutes. The same can be said if the example was the amount of silk a silk worm produced on a given day. The dice experiment is a discrete random probability because it yielded 6 possible outcomes which are 1, 2, 3, 4, 5, and 6. The number that the die landed on after each roll is “x” or the random variable. The discrete random probability is a countable number because the dice only has 6 sides. The experiment produced three 1’s, four 2’s, five 3’s, two 4’s, four 5’s and two 6’s. The experiment is a probability distribution because all six sides had the same “chance” to land on its side under the “set” conditions of 20 rolls. This is not a binomial probability because it has more than two possible outcomes. The outcome of the die has 6 possibilities or a 1 out of 6 chance to land on its side which would disqualify it as binomial. A binomial probability only deals with successes or failures. This type of experiment either does something or it doesn’t, there is no in between. References
Stat Trek, Probability Distributions,...
Please join StudyMode to read the full document