Statistics question Continous Random Variables

In summary, the first problem asks for the mean, variance, quartiles, and cdf for a distribution that is defined as f(x) = 2x, 0 ≤ x < 1. The second problem asks for the pdf and the formulas for p.d.f and c.d.f for a uniform distribution on a space [a,b].
  • #1
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Homework Statement



1) Let X have the p.d.f f(x) = 3(1-x)2, 0≤x<1. Compute:

a) P(0.1 < X < 0.5)

etc...


2) Find the mean and variance, and determine the 90th percentile , of each of the distributions given by the following densities:

a) f(x) 2x, 0≤0<0
etc..

3) Find the 50th percentile ( median ), the 25th percentile ( first quartile ), the 75th percentile ( third quartile ), and the 90th percentile ( also called the ninth decile ) for the following densities:

a)4x3 , 0≤x<1
etc..

Consider the uniform ( rectangular ) distribution on the space [a,b), where a<b, with p.d.f

f(x) 1/b-a' a≤x<b


a) Obtain the cumulative distribution function F(x). Determine the median and the first and third quartiles, and calculate the mean and variance.



I really just need to know which formulas I use here. The book has several pages deriving formulas, I can't see the wood for the trees. :frown:
 
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  • #2
You shouldn't need too many formulas, especially not for the first question. What is the definition of a pdf? What are the definitions of mean, variance, quartiles, and cdf? Show us some of your work so we can help.
 
  • #3
What you mean? I don't know how to start/do it. Those are all different problems. And I don't know how to calculate the mean and variance for one number ( one you plug in the number for the x ). That must be the mean, then. And you mean the formula for p.d.f and c.d.f ?

2) a..is supposed to be f(x) = 2x, 0 ≤ x < 1.
 
Last edited:
  • #4
Just do a Cuil or Google search for mean, variance of a continuous random variable to
find out the formulas, and get back to us.
 
  • #5
Let's just worry about the first problem to begin. You really need the definition of a pdf to do this problem. How do you express [itex] \mathbf{P}\{X \in A\}[/itex] for a set A in terms of the pdf?

Write out the definitions mentioned above and then try to use them to solve the problems. We can't help you further until you show some work.
 

Related to Statistics question Continous Random Variables

What is a continuous random variable?

A continuous random variable is a type of variable in statistics that can take on an infinite number of values within a specified range. This means that the variable can take on any value within a given interval, rather than being limited to specific discrete values like whole numbers.

How is a continuous random variable different from a discrete random variable?

A discrete random variable can only take on specific, separate values, while a continuous random variable can take on any value within a given range. For example, the number of children in a family is a discrete random variable (can only be a whole number), while the height of a person is a continuous random variable (can be any value within a range).

What is the probability density function for a continuous random variable?

The probability density function (PDF) for a continuous random variable is a function that describes the relative likelihood of a variable taking on a certain value. It is often represented graphically as a curve, and the area under the curve represents the probability of the variable falling within a certain range of values.

How do you calculate the mean and variance of a continuous random variable?

To calculate the mean of a continuous random variable, you multiply each possible value by its corresponding probability, and then add all of these products together. The variance can be calculated by taking the mean of the squared deviations from the mean.

What are some examples of continuous random variables?

Some examples of continuous random variables include height, weight, time, temperature, and stock prices. These variables can take on any value within a certain range and are not limited to specific discrete values.

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