What is the correct probability for P(3<X<4|X>1)?

In summary, the conversation involves a question about the probability of a computer choosing a point on a scale of 2 to 5 in a non-uniform way, using the density function f(x)=C*(1+x). The conversation includes finding the value of C, solving for the probability using conditional probability, and discussing a small mistake in the calculation of the denominator, which should be 1 instead of 32/27. The correct answer is 1/3 or 9/27.
  • #1
Yankel
395
0
Hello, I have this question, which I think I solve correctly, but I am getting the wrong answer.

X represent the point that the computer chooses on a scale of 2 to 5 (continuous scale) in a non-uniform way using the density:

f(x)=C*(1+x)

what is the probability P(3<X<4|X>1) ?

I solved the integral from 2 to 5 to find that C=2/27

Then using this value I did conditional probability P(3<X<4)/P(X>1). The nominator was 1/3 and the denominator was 32/27, my final result is then 9/32. The answer I have with the question is 9/27. Which one is wrong then ? Can you assist me please? Thank you in advance !
 
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  • #2
I have

\(\displaystyle \displaystyle c\times \int_2^5 x+1 ~dx = 1\)

\(\displaystyle \displaystyle c\times \left[ \frac{x^2}{2}+x\right]_2^5 = 1\)

\(\displaystyle \displaystyle c\times \left(\left[ \frac{5^2}{2}+5\right]-\left[ \frac{2^2}{2}+2\right]\right) = 1\)

\(\displaystyle \displaystyle c\times \left(\left[ 12.5+5\right]-\left[ 2+2\right]\right) = 1\)

\(\displaystyle \displaystyle c\times \left(\left[ 17.5\right]-\left[ 4\right]\right) = 1\)

\(\displaystyle \displaystyle c = \frac{1}{13.5} = \frac{2}{27}\)
 
  • #3
Yankel said:
Hello, I have this question, which I think I solve correctly, but I am getting the wrong answer.

X represent the point that the computer chooses on a scale of 2 to 5 (continuous scale) in a non-uniform way using the density:

f(x)=C*(1+x)

what is the probability P(3<X<4|X>1) ?

I solved the integral from 2 to 5 to find that C=2/27

Then using this value I did conditional probability P(3<X<4)/P(X>1). The nominator was 1/3 and the denominator was 32/27, my final result is then 9/32. The answer I have with the question is 9/27. Which one is wrong then ? Can you assist me please? Thank you in advance !

Hi Yankel,

I got the same thing for $C$ and the same numerator since $P([3<X<4] \cap [X>1])=P(3<X<4)$. For the denominator though it seems like you used this integral:
$$ \frac{2}{27} \int_{1}^{5} (1+x) \, dx = \frac{32}{27}$$
Remember though that any probability must be between 0 and 1, so this isn't a valid answer. The small mistake comes from the fact that $P(X<2)=0$ and $P(X>5)=0$, so $P(X>1)=P(2<X<5)=1$. For some reason the book chose to write the answer as 9/27 instead of 1/3, but those are equivalent answers. :)
 
  • #4
Oh...how did I miss this...typing numbers without thinking (Doh)

thanks !
 

FAQ: What is the correct probability for P(3<X<4|X>1)?

What is a continuous random variable?

A continuous random variable is a type of variable in statistics that can take on any value within a given range. Unlike discrete random variables, which can only take on whole number values, continuous random variables can take on any decimal value. They are often used to represent measurements or quantities that can vary infinitely.

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

The main difference between a continuous and discrete random variable is the type of values they can take on. A continuous random variable can take on any value within a given range, while a discrete random variable can only take on whole number values. Another key difference is that continuous random variables are measured on a scale, whereas discrete random variables are counted.

What is the probability distribution of a continuous random variable?

The probability distribution of a continuous random variable is represented by a probability density function (PDF). This function shows the probability of a continuous random variable falling within a certain range of values. The total area under the PDF curve is equal to 1, representing the total probability of all possible outcomes.

How is the mean and variance of a continuous random variable calculated?

To calculate the mean of a continuous random variable, you would need to take the sum of all possible values multiplied by their respective probabilities. For the variance, you would need to calculate the difference between each value and the mean, square the differences, and then multiply by their respective probabilities. The sum of these values gives the variance.

What are some real-life examples of continuous random variables?

Some common examples of continuous random variables include height, weight, temperature, and time. These variables can take on an infinite number of values within a given range and are often measured using a scale or unit of measurement. Other examples include prices, distances, and blood pressure.

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