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If X = time for car to be ready,

Z = (X - m)/s = (60 - 70)/9 = -1.11... Then Prob(Z < -1.111...) = 0.13326

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Q: Mean is 70minutes varience is 81minutes. what is the probability of that the car will be ready within 60 minutes?
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What is the probability of a class running between 51.25 and 51.5 minutes if the uniform distribution is between 50 and 52?

The probability is (51.5-51.25)/(52-50) = 0.25/2 = 0.125


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.


What is it called when you find the probability that something won't happen?

Its called confidence. For instance a girl kept her keys on the dinning table where other friends saw her keeping it, now when she comes after a gap of two minutes after serving herself, the keys were gone, five people checked the entire flat, it was nowhere. Then you check your flat, its not there, someone in the morning comes to return the keys. What is the point of doing that when one had to break the lock a day before.Alternate Answer:Probability is a statistics based number that expresses numerically, and as a result of scientific measure and study, the likelihood that an event will occur. the process of arriving at a measure of likelihood of whether or not an event will or will not happen is called "developing a theory of probability." This number can be represented by either a ratio, or by a percentage. For example 1:1 would be ratio of probability that if you put your keys on the dining room table, and nobody else is at home, the keys will stay on the table. Introducing additional degrees of freedom to the equation, such as other people of nefarious backgrounds, or even a cat or a parrot, will alter the statistical probability that the keys will remain stationary. The lower the ratio, it becomes less statistically likely, or probable, that the keys will remain stationary. For example, our theory of probability may state that "With a kitten in the house that likes to get on top of the furniture and play with shiny things, the likelihood of the keys remaining stationary is 100,000:1; or very nearly 0%" Alternately, with a slight tweak to how the theoretical probability is stated, we can take the same exact number, invert it, and provide an inverse statistical probability. For example, we may say that, based on the criminal background of our house guest, the statistical probability is 100% that you will have to have new keys made for your apartment.


Probability and queuing theory?

I will rephrase your question, as to "What relationship does queueing theory and probability therory?" Queueing theory is the mathematical study of waiting lines See: http://en.wikipedia.org/wiki/Queueing_theory Wait times, by their nature, are uncertain but can be represented by probability distributions. From a distribution, I may be able to tell that the chance of waiting more than 5 minutes for service is 10%, or that there is a 95% chance that my complete time in a facility (service time and wait time) is less than 15 minutes. On the other side, queueing theory may determine how often those responsible for service have no customers. The theory has broad applications, ranging from computer networks, telephony systems, delivery of goods and services (such as mail, home repair, etc) to an area and customer service in any location where people might stand in line. Traffic analysis uses queueing theory extensively. The "forward" analyses begins with an assumed probability distribution. Given probability distributions that are thought to describe certain activities (number of customers arriving in a particular time span, time spent with each customer and special events -frequency of events and time spent on special events), the distribution of waiting times can be determined mathematically. Thus, probability theory provides the basis (distribution and mathematical theory) for queueing applications. Today, more complex queueing problems are solved by Monte-Carlo simulation, which after thousands (or hundreds of thousands) of repeated runs, can provide nearly the same accuracy of statistics and distributions as those generated from purely mathematical solution. More broadly, queueing modeling and theoretical solutions are within stochastic process analysis.


How many minutes in 10 years?

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Related questions

What is the probability that a machine take an average of 10 minutes to complete a task with a standard deviation of 1.5 minuetes What is the probability that task will take 8 and 11 minutes?

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Does acceleration have to be measured in seconds or can it be measured in hours or minutes?

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What is the probability of a class running between 51.25 and 51.5 minutes if the uniform distribution is between 50 and 52?

The probability is (51.5-51.25)/(52-50) = 0.25/2 = 0.125


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Harry does English and maths homework each week it for a total of two and a half hours with 80 minutes being English - how do you solve to see how much time he spends on maths?

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