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What is the difference between probability distribution functions and probability density functions?
Wiki User
∙ 9y ago
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%.
Wiki User
∙ 9y ago
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What the difference between a probability distribution and a probability function?
None. The full name is the Probability Distribution Function (pdf).
What the difference and relationship between a probability distribution and a probability function?
They are the same. The full name is the Probability Distribution Function (pdf).
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.
What is the difference between a probability density curve and cummulative distribution function?
what is density curve
What is the difference between the normal distribution and Poisson distribution?
The normal distribution is a continuous probability distribution that describes the distribution of real-valued random variables that are distributed around some mean value.The Poisson distribution is a discrete probability distribution that describes the distribution of the number of events that occur within repeated fixed time intervals, where the mean frequency is a known value, and each interval is independent of the prior interval(s)/event(s).
What the difference between a probability distribution and a probability function?
None. The full name is the Probability Distribution Function (pdf).
What the difference and relationship between a probability distribution and a probability function?
They are the same. The full name is the Probability Distribution Function (pdf).
What is the important similarity between the uniform and normal probability distribution?
They are both continuous, symmetric distribution functions.
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.
What is the difference between a probability density curve and cummulative distribution function?
what is density curve
What is difference between skew binomial and symmetric binomial distribution?
The skew binomial distribution arises when the probability of a particular event is not a half.
What is an important difference between the uniform and normal probability distributions?
The uniform distribution is limited to a finite domain, the normal is not.
Distinguish between binomial distribution and normal distribution?
Normal distribution is the continuous probability distribution defined by the probability density function. While the binomial distribution is discrete.
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.
How does a discrete probability distribution differ from a continuous probability distribution?
A discrete probability distribution is defined over a set value (such as a value of 1 or 2 or 3, etc). A continuous probability distribution is defined over an infinite number of points (such as all values between 1 and 3, inclusive).
What is the difference between a probability mass function and a probability density function?
The probability mass function is used to characterize the distribution of discrete random variables, while the probability density function is used to characterize the distribution of absolutely continuous random variables. You might want to read more about this at www.statlect.com/prbdst1.htm (see the link below or on the right)
What is the difference between the normal distribution and Poisson distribution?
The normal distribution is a continuous probability distribution that describes the distribution of real-valued random variables that are distributed around some mean value.The Poisson distribution is a discrete probability distribution that describes the distribution of the number of events that occur within repeated fixed time intervals, where the mean frequency is a known value, and each interval is independent of the prior interval(s)/event(s).
