Cumulative distribution function

In summary, the conversation discussed finding the value of k for a continuous random variable X, the probability density function of X, and the median of the square root of X. It also touched on finding the probability of a certain number of independent observations being less than a given value and finding the conditional probability of one event given another. The key equations used were the definition of a cumulative distribution function, taking derivatives, and setting up integral expressions.
  • #1
drawar
132
0

Homework Statement


The continuous random variable X has cumulative distribution function given by
[tex]F(x) = \left\{ {\begin{array}{*{20}c}
0 & {x \le 0} \\
{\frac{{x^2 }}{k}} & {0 \le x \le 1} \\
{ - \frac{{x^2 }}{6} + x - \frac{1}{2}} & {1 \le x \le 3} \\
1 & {x \ge 3} \\
\end{array}} \right.[/tex]
(i) Find the value of k
(ii) Find the probability density function of X and sketch its graph
(iii) Find the median of [tex]\sqrt X[/tex]
(iv) 10 independent observations of X are taken. Find the probability that eight of them are less than 2.
(v) Let A be the event X > 1 and B be the event X > 2. Find P(B|A)

Homework Equations


The Attempt at a Solution



I'm able to do the first 2 questions
For (i), by substitution I get k=3
For (ii), I take the derivative of F(x), then [tex]f(x) = \left\{ {\begin{array}{*{20}c}
{\frac{{2x}}{3}} & {0 \le x \le 1} \\
{ - \frac{x}{3} + 1} & {1 \le x \le 3} \\
0 & {otherwise} \\
\end{array}} \right.[/tex]
However, I have no idea how to do the rest. Any feedback is appreciated, thanks
 
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  • #2
For (iii), the median is the value M such that the random variable is equally likely to be below or above M:

[tex]\int_0^M f(x) dx = 0.5[/tex]
(Solve for M.)

For (iv), start by finding the probability that a single observation is less than 2.

For (v), start by writing down the definition of P(B|A).

P.S. Is this really "precalculus mathematics"?
 
  • #3
Thank you for pointing that out to me. Is the median of X equal to that of [tex]\sqrt X [/tex]?
 
  • #4
Oh, sorry, I overlooked the [itex]\sqrt{x}[/itex] in the question. No, in general, it won't be the same as the median of X.

You are looking for the value of M such that

[tex]P(\sqrt{X} < M) = 0.5[/tex]

Now, since X is non-negative, it follows that [itex]\sqrt{X} < M[/itex] if and only if [itex]X < M^2[/itex].

So

[tex]P(\sqrt{X} < M) = P(X < M^2)[/tex]

So what is [itex]P(X < M^2)[/itex] in terms of an integral expression involving f(x)?
 
  • #5
That definitely helps. Thanks!
 

1. What is a Cumulative Distribution Function (CDF)?

A Cumulative Distribution Function (CDF) is a mathematical function that represents the probability distribution of a continuous random variable. It maps the probability of a random variable taking on a value less than or equal to a given value.

2. How is a CDF different from a Probability Distribution Function (PDF)?

A CDF represents the cumulative probability of a random variable, while a PDF represents the probability of a specific value occurring. In other words, a CDF shows the probability of a random variable being less than or equal to a certain value, while a PDF shows the probability of a random variable being exactly equal to a certain value.

3. What is the shape of a CDF graph?

The shape of a CDF graph is a continuous and increasing curve that starts at 0 on the left side and ends at 1 on the right side. The curve never decreases, as the probability of a random variable being less than or equal to a certain value can only increase or stay the same.

4. How is a CDF used in statistics?

CDFs are used to analyze and understand the probability distribution of a random variable. They can be used to calculate the probability of a random variable falling within a certain range of values, or to determine the probability of a specific value occurring. CDFs are also used to compare different probability distributions and make predictions based on the data.

5. Can a CDF be used to calculate the mean and variance of a random variable?

Yes, the mean and variance of a random variable can be calculated from its CDF. The mean can be calculated by finding the value of the random variable that corresponds to a CDF of 0.5, and the variance can be calculated using the formula Var(X) = E(X^2) - [E(X)]^2, where E(X) is the expected value of the random variable.

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