Statistics Problem - Uniform Distribution

In summary: The min/max seems to be confusing because it represents the value of a variable that is either the lowest or the highest value among a set of values.
  • #1
planauts
86
0
Hi,

The question is: http://puu.sh/5GX2G.jpg

http://puu.sh/5GX2G.jpg

I am not exactly sure what the question is asking.


Here is the answer/solution: http://puu.sh/5GX68.png
But I am not sure what is going on.

Could someone please explain what exactly the question is asking, I can figure out the rest.

Thanks,
 
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  • #2
It's asking you to work out the cumulative distribution function (CDF) F(x). This is defined as the probability that a variate X takes on a value less than or equal to a number x, in other words F(x) = P(X ≤ x).

Now in this problem X = max(X1, X2, X3, X4), so for any x in order for X ≤ x to be true we must have X1≤ x and X2≤ x and X3≤ x and X4≤ x.

Are you OK from there?
 
  • #3
Assuming that, I can get the rest of the problem. Once you get, F(x), you can take the derivative to get f(x). To get expected you integrate x*f(x) from 0,1 and x^2 * f(x) for the variance.However, I am still confused for the first part (i.e. the cumulative function). The max seems to throw me off. There is another problem similar to this one, except it uses min.

http://puu.sh/5HsfD.jpg

-------------------------

The max/min seems to throw me off, what exactly does that represent? :S

Thanks
 
Last edited by a moderator:
  • #4
planauts said:
However, I am still confused for the first part (i.e. the cumulative function). The max seems to throw me off.
MrAnchovy gave you the answer.
X = max(X1, X2, X3, X4) ≤ x is equivalent to all Xi ≤ x.
Since the Xi are independent, P(X≤x) = P(X1≤x)P(X2≤x)P(X3≤x)P(X4≤x) = x4.
 
  • #5
How would you do min?
 
  • #6
You need to calculate
[tex] P(X_1 \geq x, X_2 \geq x, X_3 \geq x \text{ and } X_4 \geq x ) [/tex]
Can you figure out what this is?
 

Related to Statistics Problem - Uniform Distribution

1. What is a uniform distribution in statistics?

A uniform distribution in statistics is a probability distribution where all possible outcomes have an equal chance of occurring. This means that the values of a variable are spread out evenly across the range of possible values, creating a rectangular shape on a graph.

2. How is a uniform distribution different from a normal distribution?

A normal distribution, also known as a Gaussian distribution, is a symmetrical distribution in which the majority of values are clustered around the mean, with fewer values at the extremes. A uniform distribution, on the other hand, has a constant probability for all values within a given range.

3. What is the formula for calculating the mean and variance of a uniform distribution?

The mean of a uniform distribution can be calculated by taking the average of the lowest and highest values in the range. The variance can be calculated using the formula (b-a)^2/12, where 'a' is the lowest value and 'b' is the highest value in the range.

4. How can a uniform distribution be used in real-life scenarios?

A uniform distribution can be used to model situations where all outcomes are equally likely, such as rolling a fair die or selecting a random number from a set range. It can also be used in finance to model stock prices or in quality control to monitor the uniformity of manufactured products.

5. What are some common misconceptions about uniform distributions?

One common misconception is that a uniform distribution always has a rectangular shape on a graph. While this is often the case, a uniform distribution can also have a triangular or trapezoidal shape depending on the range of values. Another misconception is that a uniform distribution is always a discrete distribution, when in fact it can also be a continuous distribution if the range of values is infinite.

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