How Accurate is the CDF Calculation in This Probability Problem?

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In summary: Anyway, the summary of this conversation is that the problem involves calculating the probability of a random variable within a certain range. The given solution involves finding the pdf and integrating it, while the book's solution uses the cdf. Both approaches have some discrepancies but ultimately lead to the same answer. The confusion is due to the presence of delta functions in the pdf.
  • #1
mattmns
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[SOLVED] Probability (cdf question)

Here is the problem from the book
-----------
Let X be a random variable with distribution function (cdf)

[tex]
F(x)=\begin{cases}
0 &\text{for } x\geq 0\\
\frac{x}{8} & \text{for } 0 \leq x < 1\\
\frac{1}{4} + \frac{x}{8} & \text{for } 1 \leq x < 2\\
\frac{3}{4} + \frac{x}{12} & \text{for } 2 \leq x < 3\\
1 & \text{for } x \geq 3\end{cases}
[/tex]

Calculate [tex]P(1 \leq X \leq 2)[/tex].
----------------

This should be a simple question, but I am getting a different answer than my book is, and I believe the book is wrong (how could I be wrong? :-p).Here is what I did:

[tex]
\begin{align*}
P(1 \leq X \leq 2) & = P(1 \leq X < 2) \\
& = P(X < 2) - P(X \leq 1) \\
& = \frac{1}{4} + \frac{2}{8} - \left(\frac{1}{4} + \frac{1}{8}\right) \\
& = \frac{1}{8}
\end{align*}
[/tex]

We could also get the same answer by finding the pdf and then integrating it over the interval (1,2).The book I have gives an answer of [tex]\frac{19}{24}[/tex].

Here is what they did:

[tex]
\begin{align*}
P(1 \leq X \leq 2) & = P(X\leq 2) - P(X < 1) \\
& = F(2) - \lim_{x\to 1^-}F(x) \\
& = \frac{11}{12} - \frac{1}{8} \\
& = \frac{19}{24}\\
\end{align*}
[/tex]

In my opinion this seems doubly wrong. They used the wrong function on both, but at least they were consistent I suppose. Am I being silly here and missing something, or is the book wrong? Thanks!edit... I am looking at my book, and they have [tex]P(a < X \leq b) = F(b) - F(a)[/tex]. Now I won't argue with this, but the way it is used in the above example seems completely counterintuitive to me. I will admit my use of the cdf may be dubious for P(X < 2), but it feels right. Maybe the book is right after all. Thoughts?
 
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  • #2
There are two delta functions in your pdf as well as the continuous part. And they are both included in [1,2]. So P(1<X<2) is not equal to P(1<=X<=2).
 
  • #3
So you are saying we have a mixed distribution, and that at least one of P(X = 1), P(X = 2) is non-zero? So from looking at the graph of the cdf, I see that P(X = 1) is 1/4 and P(X = 2) is 5/12.

So if we take what I have, 1/8, and add the points I forgot we get 19/24 which is the book's answer. Thanks, I guess this problem was a little more complicated than I had originally thought.

I guess I should take a closer look at what the cdf actually looks like :redface:
 
  • #4
Yes, mind the discontinuities.
 

FAQ: How Accurate is the CDF Calculation in This Probability Problem?

What is a cumulative distribution function (CDF)?

A cumulative distribution function (CDF) is a mathematical function that describes the probability that a random variable takes on a value less than or equal to a given value. It is a way to quantify the likelihood of different outcomes of an experiment or event.

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

A CDF gives the cumulative probability of a random variable taking on a certain value or less, while a PDF gives the probability of a random variable taking on a specific value. In other words, a CDF is the integral of a PDF, and the slope of a CDF at a specific point is equal to the value of the PDF at that point.

How is a CDF used in statistics and data analysis?

CDFs are used in statistics and data analysis to understand the distribution of a dataset and to make predictions about future outcomes. They can also be used to calculate probabilities, percentiles, and other important statistics.

What is the relationship between a CDF and a percentile?

A CDF can be used to calculate the percentile of a given value in a dataset. For example, if the CDF of a dataset is 0.2 at a certain value, it means that 20% of the data falls below that value. This allows us to compare data points and understand their relative positions in a dataset.

How do you interpret a CDF graph?

A CDF graph plots the cumulative probability of a random variable on the y-axis and the corresponding values of the variable on the x-axis. It shows the overall distribution of the data and can be used to determine the likelihood of different outcomes. The steeper the slope of the CDF, the more concentrated the data is around that value. A flat line indicates that the data is evenly distributed.

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