Probability density function help

In summary, the problem involves a continuous random variable with a probability density function represented by a triangle with coordinates (0, 1) and (2, 0). The question asks for the probability of the variable being between 1 and 2. The area under the triangle represents the maximum probability and in this case, it is 1. To find the probability for the given range, we need to calculate the area of the triangle with vertices (1, 0), (1, y), and (2, 0). This can be done by finding the equation of the line or using similar triangles.
  • #1
camboguy
36
0
ok iv have been stuck on this problem for like 30 mins it says "suppose x is a continuous random variable taking values between 0 and 2 and having the probability density function below."

the graph below shows a triangle with the coordinates (0,1) (2,0)

then it ask what is the Probably P(1<= X <= 2)

iv tired doing the Pythagorean therm but guess that doesn't work, then i tried something with finding the whole triangles area but then i was stuck after that and thought that was incorrect to then i sat there and was trying to find another way but i don't know any other way.
 
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  • #2
The area under the triangle integrates (adds up) to one. That's the maximum probability anyone can get (100% certainty). It's another way of saying that with 100% probability the random variable will be between 0 and 2.

The question is asking a similar question for a narrower region; so you need to calculate the area under the triangle for that region (1 < x < 2).

See http://en.wikipedia.org/wiki/Triangle_distribution
 
  • #3
You say "a triangle with coordinates (0, 1) and (2, 0)" and you say that x varies from 0 to 2, so the third vertex is at (0,0). It's easy to calculate that the area of that triangle is (1/2)(2)(1)= 1 as EnumaElish pointed out. There is no reason to use the Pythagorean theorem because you are not interested in the length of the hypotenuse. You can either write out the equation of the line from (0,1) to (2,0) or use "similar triangles" to determine the y value when x= 1. (Since x=1 is exactly 1/2 of 2, that's almost trivial.) Then you just need to find the area of the triangle with vertices (1, 0), (1, y), and (2, 0).
 

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

A probability density function (PDF) is a mathematical function that describes the likelihood of a random variable taking on a specific value. It is often used in statistics and probability to model continuous random variables.

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

While both a PDF and a probability distribution function (PDF) represent the probability of a random variable taking on a specific value, a PDF is used for continuous random variables while a PDF is used for discrete random variables.

3. How do you interpret a PDF graphically?

A PDF is represented graphically as a continuous curve, where the area under the curve represents the probability of a random variable falling within a specific range of values. The higher the curve, the higher the likelihood of the random variable taking on values within that range.

4. What is the relationship between a PDF and a cumulative distribution function (CDF)?

The cumulative distribution function (CDF) is the integral of the PDF and represents the probability of a random variable being less than or equal to a specific value. In other words, the CDF gives the probability of a random variable falling within a certain range of values.

5. How do you use a PDF to calculate probabilities?

To calculate probabilities using a PDF, you need to integrate the PDF over a specific range of values. This will give you the probability of the random variable falling within that range. Additionally, you can use the CDF to calculate probabilities by subtracting the probabilities of the lower bound from the upper bound.

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