Functions of Continuous random variables

In summary, the problem involves finding the probability of Y being less than or equal to 1/2, where Y is defined as 1/X. The correct approach is to multiply the inequality by x^2 and manipulate it to solve for the probability, which is 0.5. The mistake in the given approach was treating x as either positive or negative, which affected the subsequent working.
  • #1
Disar
28
0
I have been working on this problem and can't seem to get the answer.

Problem:
X is a continuous random variable with a proabaility density function:

f(x) = 1/4 if -2<=x<=2
0 other wise

Let Y=1/X. Then P(Y<=1/2) = ?

This is how I approached the problem:

P(Y<=1/2)=P(1/x=1/2)
=P(X>=2)

Taking the intergral of f(x) with limits of integration -2,2.

My answer is 1.

However the answer given is 1/2.

Any ideas.
 
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  • #2
Reply

Hmm.. Since it is given in the question that x runs from -2 to 2 only, how can [tex] P(X\geq2) [/tex] be 1? Shouldn't it be 0?

Well, your working [tex] P(Y\leq \frac{1}{2}) = P(\frac{1}{X}\leq \frac{1}{2}) [/tex] is correct. The error lies in your subsequent statement "which is in turn equal to [tex] P(X\geq2) [/tex]"

You see, since x runs from -2 to 2, it can take both positive and negative values. So, should we treat x as positive or negative, knowing that the subsequent working will be affected by our decision? (i.e. If x is negative, then we will need to change the inequality sign when cross-multiplying)

To solve this problem, we multiply the inequality by [tex] x^2 [/tex], since we know for sure that this expression is positive (x cannot be equal to zero if y is to be real)

The resulting inequality will be quadratic in nature, and some algebraic manipulation should get you the desired probability of 0.5.

All the best!
 
Last edited:

What are continuous random variables?

Continuous random variables are numerical variables that can take on an infinite number of values within a specific range. They are used to represent measurements and observations that can take on any value within a range, rather than being limited to specific discrete values.

What is the difference between continuous and discrete random variables?

The main difference between continuous and discrete random variables is that continuous variables can take on any value within a specific range, while discrete variables can only take on specific, separate values. For example, height is a continuous random variable because it can take on any value within a range, while the number of siblings a person has is a discrete random variable because it can only take on specific values (e.g. 0, 1, 2, etc.).

What are some examples of continuous random variables?

Some examples of continuous random variables include height, weight, temperature, time, and distance. These variables can take on any value within a specific range and are often measured on a continuous scale.

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

A probability density function (PDF) for a continuous random variable is a mathematical function that describes the probability distribution of the variable. It shows the relative likelihood of different values occurring within a given range of the variable. The area under the curve of a PDF represents the probability of the variable falling within a certain range of values.

How do you calculate the expected value of a continuous random variable?

The expected value of a continuous random variable is calculated by multiplying each possible value of the variable by its corresponding probability and then summing these values. This can be represented mathematically as E(X) = ∫xf(x)dx, where x represents the possible values of the variable and f(x) represents the probability density function.

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