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somecelxis
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somecelxis said:Homework Statement
Here's the question:
Given X is a discrete random variable. E(X-2) = 1/3 , VAR(X-2) = 20/9 .
Detrmine value of E(X^2)
the ans is 23/3 . but i ended up getting 3 . why i am wrong?
Homework Equations
The Attempt at a Solution
somecelxis said:Homework Statement
Here's the question:
Given X is a discrete random variable. E(X-2) = 1/3 , VAR(X-2) = 20/9 .
Detrmine value of E(X^2)
the ans is 23/3 . but i ended up getting 3 . why i am wrong?
You made a mistake when expanding (x-2)2.
ehild
ehild said:somecelxis said:Homework Statement
Here's the question:
Given X is a discrete random variable. E(X-2) = 1/3 , VAR(X-2) = 20/9 .
Detrmine value of E(X^2)
the ans is 23/3 . but i ended up getting 3 . why i am wrong?
You made a mistake when expanding (x-2)2.
ehild
I guess I would have caught that if the OP had typed out his work, but since I never read attached thumbnails I missed it.
A discrete probability distribution is a statistical distribution that shows the probability of a discrete random variable taking on a specific value. It is used to represent the probability of different outcomes in a discrete set of events, such as rolling a dice or flipping a coin.
A discrete probability distribution deals with discrete random variables, which can only take on certain values, while a continuous probability distribution deals with continuous random variables, which can take on any value within a given range. For example, the number of heads when flipping a coin is a discrete random variable, while the height of a person is a continuous random variable.
Some common examples of discrete probability distributions include the binomial distribution, the Poisson distribution, and the geometric distribution. These distributions are often used to model real-world phenomena, such as the number of successes in a series of independent trials or the number of customers arriving at a store in a given time period.
In a discrete probability distribution, the probability of a specific outcome is determined by dividing the number of favorable outcomes by the total number of possible outcomes. This is known as the probability mass function, which assigns a probability to each possible value of the random variable.
Discrete probability distributions are essential in scientific research as they allow researchers to model and analyze the likelihood of different outcomes in discrete events. This enables them to make predictions and draw conclusions based on data, helping to inform decision-making processes and advance scientific understanding.