Probability distribution function question

In summary, a PC is programmed to generate "random" numbers from [0,1] based on a continuous random variable X with a distribution function F(x). This function has different values for different ranges of x, such as 0 for x < 0 and 1 for x ≥ 1. The problem asks for the probabilities of certain ranges of X, and the student has a question about the value of F(x) for x > 1. It is clarified that F(x) is the standard notation for the cumulative distribution function and the student realizes their mistake.
  • #1
atrus_ovis
101
0

Homework Statement


A PC generates "random" numbers from [0,1], programmed such that
the distribution function F(x) of a continuous random variable X, which is satisfies the formula:

F(x) =
0 , x<0
x , 0<=x<0.25
0.25 , 0.25<=x<0.5
x2, 0.5<=x<1
1 , 1<=x

THe problem then asks the values of probabilities in ranges of X.

Homework Equations


-

The Attempt at a Solution


My question is, why is F(x) , x>1 = 1?
Isn't that, you know, non sensical?

And, how, for example will i measure the P(x>.75), when F(x)=1 for x>1 ?
(edit: or is big F of x, a standard notation for the cumulative distr. function?)
 
Last edited:
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  • #2
Ah, never mind.
It is the cumulative distribution function, both my teacher and textbook had the great idea of calling it "ditribution function"
Dammit.

delete if you want
 
Last edited:

1. What is a probability distribution function (PDF)?

A probability distribution function (PDF) is a mathematical function that describes the likelihood of a random variable taking on a certain value or set of values. It is used to model and analyze the behavior of random events.

2. How is a PDF different from a probability density function (PDF)?

A PDF and a probability density function (PDF) are essentially the same thing. The only difference is that the term PDF is used more commonly in the context of discrete random variables, while the term probability density function is used more commonly in the context of continuous random variables.

3. What are the properties of a probability distribution function?

A probability distribution function must satisfy two properties: it must always be positive (since probabilities cannot be negative) and the sum of all probabilities must equal 1 (since the total probability of all possible outcomes is 1).

4. How do you calculate the mean and variance of a probability distribution function?

The mean of a probability distribution function is calculated by multiplying each possible value of the random variable by its corresponding probability and summing up these values. The variance is calculated by taking the sum of the squared differences between each possible value and the mean, multiplied by their corresponding probabilities.

5. What are some common probability distribution functions?

Some common probability distribution functions include the normal distribution, binomial distribution, Poisson distribution, and exponential distribution. These functions are often used to model real-world phenomena in fields such as statistics, economics, and physics.

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