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What is the probability that a Poisson random variable x is equal to 5...?
Wiki User
∙ 13y ago
It depends on the parameter - the mean of the distribution.
Wiki User
∙ 13y ago
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If the outcomes of a random variable follow a Poisson distribution then their?
means equal the standard deviation
What is commulative frequency distribution?
The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x. The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x. The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x. The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x.
What sample selected in such a way that each member of the population has an equal probability of being included?
It is a simple random sample.
Are the mean and variance equal in the poisson distribution?
yes
What is the probability that a marble drawn at random is not red if a jar contains 3 red 4 blue and 2 white marbles?
i think 6 out of 9 right? then you simplify to equal 1 out of 3.:)
If the outcomes of a random variable follow a Poisson distribution then their?
means equal the standard deviation
How often the value of a random variable is at or below a certain value.?
The probability of a random variable being at or below a certain value is defined as the cumulative distribution function (CDF) of the variable. The CDF gives the probability that the variable takes on a value less than or equal to a given value.
How do calculate the probability of at most?
For a discrete variable, you add together the probabilities of all values of the random variable less than or equal to the specified number. For a continuous variable it the integral of the probability distribution function up to the specified value. Often these values may be calculated or tabulated as cumulative probability distributions.
Difference between variables and probability distribution?
Assuming you mean random variable here. A random variable is term that can take have different values. for example a random variable x that represent the out come of rolling a dice, that is x can equal 1,2,3,4,5,or 6. Think of probability distribution as the mapping of likelihood of the out comes from an experiment. In the dice case, the probability distribution will tell you that there 1/6 the time you will get 1, 2,3....,or 6. this is called uniform distribution since all the out comes have that same probability of occurring.
Is the expected value of a discrete random variable equal to the mean of the random variable?
yes?
What is a uniform probability distribution?
It is a probability distribution where when all of the values of a random variable occur with equal probability. Say X is the random variable, such as what number shows up when we roll a die. There are 6 possible outcomes, each with a 1/6 probability of showing up. If we create a probability distribution where X= 1,2,3,4,5, or 6, we note P(X=k)=1/k where k is any number between 1 and 6 in this case. The graph will be a rectangle.
What is a uniform distribution?
It is a probability distribution where when all of the values of a random variable occur with equal probability. Say X is the random variable, such as what number shows up when we roll a die. There are 6 possible outcomes, each with a 1/6 probability of showing up. If we create a probability distribution where X= 1,2,3,4,5, or 6, we note P(X=k)=1/k where k is any number between 1 and 6 in this case. The graph will be a rectangle.
What is uniform distribution?
It is a probability distribution where when all of the values of a random variable occur with equal probability. Say X is the random variable, such as what number shows up when we roll a die. There are 6 possible outcomes, each with a 1/6 probability of showing up. If we create a probability distribution where X= 1,2,3,4,5, or 6, we note P(X=k)=1/k where k is any number between 1 and 6 in this case. The graph will be a rectangle.
What is the difference between probability distribution functions and probability density functions?
Probability density function (PDF) of a continuous random variable is a function that describes the relative likelihood for this random variable to occur at a point in the observation space. The PDF is the derivative of the probability distribution (also known as cummulative distriubution function (CDF)) which described the enitre range of values (distrubition) a continuous random variable takes in a domain. The CDF is used to determine the probability a continuous random variable occurs any (measurable) subset of that range. This is performed by integrating the PDF over some range (i.e., taking the area under of CDF curve between two values). NOTE: Over the entire domain the total area under the CDF curve is equal to 1. NOTE: A continuous random variable can take on an infinite number of values. The probability that it will equal a specific value is always zero. eg. Example of CDF of a normal distribution: If test scores are normal distributed with mean 100 and standard deviation 10. The probability a score is between 90 and 110 is: P( 90 < X < 110 ) = P( X < 110 ) - P( X < 90 ) = 0.84 - 0.16 = 0.68. ie. AProximately 68%.
What is the difference between a random number and random variable?
A random number is also called a random deviate or pseudo random number. It is a value that is generated by a computer, given a probability distribution. The uniform random number is distributed according to the uniform distribution with values from 0 to 1 and is often used to generate other numbers that follow other distributions. The relative frequency plot of random numbers generated using a particular distribution may not be exactly equal to the particular distribution, due to the limited number of points. But, as the number of random deviates increases, the frequency plot will more closely approximate the given distribution. Random variable have both an intuitive and mathematical definitions. Probability theory is based on sets of events. A throw of coin will have certain outcomes. The random variable, X, links or maps these events to values. A coin can come up heads, so the mapped random variable of this outcome can be 0, and for tails, the random variable of this outcome can be 1. The mathematical definition is a bit more complex- see related links. A random variable is associated with what is considered a random process. If we know the outcome without any uncertainty, we would call it an deterministic process and the outcomes deterministic variables.
What is commulative frequency distribution?
The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x. The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x. The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x. The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x.
Sample selected in such a way that each member of the population has an equal probability of being included?
random sample
