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How do the highest and lowest possible values for the variable compare in their probability of occurring to the values in the middle of the distribution?
Wiki User
∙ 10y ago
The answer will depend on the skewness of the distribution.
The Poisson distribution is defined for non-negative integers: 0, 1, 2, 3, 4 etc. So the lowest value is 0.
For a Poisson distribution with parameter l=1 (when it is very skew), the probability of the lowest two values, 0 and 1, is 0.368 each and the probability tails off rapidly for higher values.
The answer will depend on the skewness of the distribution.
The Poisson distribution is defined for non-negative integers: 0, 1, 2, 3, 4 etc. So the lowest value is 0.
For a Poisson distribution with parameter l=1 (when it is very skew), the probability of the lowest two values, 0 and 1, is 0.368 each and the probability tails off rapidly for higher values.
The answer will depend on the skewness of the distribution.
The Poisson distribution is defined for non-negative integers: 0, 1, 2, 3, 4 etc. So the lowest value is 0.
For a Poisson distribution with parameter l=1 (when it is very skew), the probability of the lowest two values, 0 and 1, is 0.368 each and the probability tails off rapidly for higher values.
The answer will depend on the skewness of the distribution.
The Poisson distribution is defined for non-negative integers: 0, 1, 2, 3, 4 etc. So the lowest value is 0.
For a Poisson distribution with parameter l=1 (when it is very skew), the probability of the lowest two values, 0 and 1, is 0.368 each and the probability tails off rapidly for higher values.
Wiki User
∙ 10y ago
Wiki User
∙ 10y agoThe answer will depend on the skewness of the distribution.
The Poisson distribution is defined for non-negative integers: 0, 1, 2, 3, 4 etc. So the lowest value is 0.
For a Poisson distribution with parameter l=1 (when it is very skew), the probability of the lowest two values, 0 and 1, is 0.368 each and the probability tails off rapidly for higher values.
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What does it mean when you say a variable is normally distributed?
It means that the probability distribution function of the variable is the Gaussian or normal distribution.
What is the likelihood of the probability 0 to infinity?
The answer depends on the probability distribution function for the random variable.
What is the difference between probability distribution and probability density function?
A probability density function assigns a probability value for each point in the domain of the random variable. The probability distribution assigns the same probability to subsets of that domain.
The definition of mode in math?
Mode is the most frequently occurring value in a data set. See related link. Note that in statistics, the definition is related to the data collected. In probability, the definition is related to the probability distribution which describes the random variable.
Define a normal random variable?
I have included two links. A normal random variable is a random variable whose associated probability distribution is the normal probability distribution. By definition, a random variable has to have an associated distribution. The normal distribution (probability density function) is defined by a mathematical formula with a mean and standard deviation as parameters. The normal distribution is ofter called a bell-shaped curve, because of its symmetrical shape. It is not the only symmetrical distribution. The two links should provide more information beyond this simple definition.
What are the 2 conditions that determine a probability distribution?
The value of the distribution for any value of the random variable must be in the range [0, 1]. The sum (or integral) of the probability distribution function over all possible values of the random variable must be 1.
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.
What gives the probability of each random variable?
The marginal probability distribution function.
What would -2 standard deviation below the mean be?
It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.
What does it mean when you say a variable is normally distributed?
It means that the probability distribution function of the variable is the Gaussian or normal distribution.
What is the likelihood of the probability 0 to infinity?
The answer depends on the probability distribution function for the random variable.
What is the meaning of random variable in probability distribution?
It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.
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.
If an event has a 1 in 4 chance of occurring what is the probability of it happening to one individual in a sample of five?
If we assume that the probability of an event occurring is 1 in 4 and that the event occurs to each individual independently, then the probability of the event occurring to one individual is 0.3955. In order to find this probability, we can make a random variable X which follows a Binomial distribution with 5 trials and probability of success 0.25. This makes sense because each trial is independent, the probability of success stays constant for each trial, and there are only two outcomes for each trial. Now you can find the probability by plugging into the probability mass function of the binomial distribution.
What is the difference between probability distribution and probability density function?
A probability density function assigns a probability value for each point in the domain of the random variable. The probability distribution assigns the same probability to subsets of that domain.
Difference between a random variable and a probability distribution is?
A random variable is a variable that can take different values according to a process, at least part of which is random.For a discrete random variable (RV), a probability distribution is a function that assigns, to each value of the RV, the probability that the RV takes that value.The probability of a continuous RV taking any specificvalue is always 0 and the distribution is a density function such that the probability of the RV taking a value between x and y is the area under the distribution function between x and y.
Is the normal probability distribution applied to a continuous random variable?
Yes.
