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Is a discrete probability distribution the only thing that can have an infinite number of outcomes?
Wiki User
∙ 9y ago
No, the exact opposite is true.
Wiki User
∙ 9y ago
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If you have 15 NFL games with 2 outcomes eithe win or lose how many different combinaions are there?
32,768 different outcomes
If you roll two standard dice what is the probability that the sum will be a prime number?
15/36 Explanation: dice 1 can roll 6 possible numbers dice 2 can roll 6 possible numbers possible outcomes: 6x6= 36 prime outcomes = 2, 3, 5, 7, 11 2-->(1,1)-->1 3-->(1,2) and (2,1)-->2 5-->(1,4) (4,1) (2,3) and (3,2) -->4 7-->(1,6) (6,1) (2,5) (5,2) (3,4) and (4,3)-->6 11->(5,6) (6,5)-->2 1+2+4+6+2=15 15/36
Why is 529 not a prime number?
A prime number is defined as in simpler terms a number that is only divisible by the number 1 and itself. (Two possible outcomes) However, 529 is not prime. 529 = 23 * 23 529 = 1 * 529 529 = 529 * 1 (Three outcomes discovered)
How do you convert 11.27 percentile into percentage?
A percentile is a measure of the relative position of the outcome of an experiment. 11.27 percentile means that 11.27% of the outcomes were lower and 88.73 were higher. However, it does not provide any information on the level of that outcome.
What is the difference between output and result?
Process produce results. Some of these are tangible and measurable at the time they are generated. These we call Process Outputs. There are other results that are not measurable until long after the outputs have been delivered and often long after they have been used. These can be considered to be the impact of the process on its surroundings An output of a process may have a detrimental affect on the environment. Satisfaction of either customers or employees is an impact not an output. However, processes can only be designed to deliver outputs because the outputs are measured before they emerge from the process, whereas, impacts arise long after the process has delivered its outputs and therefore cannot be used to control process performance. Any attempt to do so would induce an erratic performance. In reviewing the performance of a process we can note whether the outputs and the impacts were as expected. What we are doing is reviewing the process outcomes therefore we can consider outcomes to be outputs + impacts. Results can therefore be considered to be a general term because outputs are results, impacts are results and outcomes are results. So when you ask what results does a process produce the answer can be in terms of its outputs, impacts or outcomes. But when you ask what results does a process deliver the answer should strictly be in terms of its outputs. For more information see Quality Management Essentials
What are two requirements for a discrete probability distribution?
Not sure about only two requirements. I would say all of the following:there is a finite (or countably infinite) number of mutually exclusive outcomes possible,the probability of each outcome is a number between 0 and 1,the sum of the probabilities over all possible outcomes is 1.The Poisson distribution, for example, is countably infinite.
A complete probability distribution is always an objective listing of all possible events Since it is impossible to list all the possible outcomes from a single event probability distributions are o?
Your question is not clear, but I will attempt to interpret it as best I can. When you first learn about probability, you are taught to list out the possible outcomes. If all outcomes are equally probable, then the probability is easy to calculate. Probability distributions are functions which provide probabilities of events or outcomes. A probability distribution may be discrete or continuous. The range of both must cover all possible outcomes. In the discrete distribution, the sum of probabilities must add to 1 and in the continuous distribtion, the area under the curve must sum to 1. In both the discrete and continuous distributions, a range (or domain) can be described without a listing of all possible outcomes. For example, the domain of the normal distribution (a continuous distribution is minus infinity to positive infinity. The domain for the Poisson distribution (a discrete distribution) is 0 to infinity. You will learn in math that certain series can have infinite number of terms, yet have finite results. Thus, a probability distribution can have an infinite number of events and sum to 1. For a continuous distribution, the probability of an event are stated as a range, for example, the probability of a phone call is between 4 to 10 minutes is 10% or probability of a phone call greater than 10 minutes is 60%, rather than as a single event.
How many experimental outcomes are possible for the binomial and the Poisson distributions?
The binomial distribution is a discrete probability distribution. The number of possible outcomes depends on the number of possible successes in a given trial. For the Poisson distribution there are Infinitely many.
Is the binomial distribution is a continuous distribution?
No it is a "discrete" distribution because the outcomes can only be integers.
How do you construct a probability distribution?
First decide whether the event space is discrete or continuous.For a discrete event space, for each outcome in the space assign a probability: a number in the interval [0, 1] such that the sum of probabilities for all outcomes is 1. The mapping from the event space to the probabilities is the probability distribution function.The procedure for a continuous event space is analogous: the sum is replaced by the integral.
Is the range of outcomes infinite in normal distribution?
1
How do you obtain a probability distribution?
Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.
What does it mean for a probability to be fair?
A probability is fair if there is no bias in any of the possible outcomes. Said another way, all of the possible outcomes in a fair distribution have an equal probability.
What is a listing of all possible outcomes of an experiment and their corresponding probability of occurrence is called?
It is the probability distribution.
Is in the normal distribution the total area beneath the curve represent the probability for all possible outcomes for a given event?
Yes. The total area under any probability distribution curve is always the probability of all possible outcomes - which is 1.
Why is the probability of an event always a number between 0 and 1?
The probability of an event is defined as the ratio of favourable outcomes to total outcomes. In the case of discrete distributions these will be represented by numbers, while for continuous distribution they will be measured as areas. In either case, the first measure is non-negative and the second is positive and so the probability is greater than 0. Also, the number of favourable outcomes cannot be greater than the total so the probability must be at most 1.
Which approach to probability requires equally likely outcomes?
The modelling of a probability distribution function for an event from a theoretical approach.
