Probability related to cumulative distribution function

  • #1
songoku
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Homework Statement
Please see below
Relevant Equations
F(X) = P(X ≤ x)
1697385605593.png


I have tried to answer all the questions but I am not that sure with my answer.

1697386871082.png

That's the graph of ##F_X (x)## (I think)

(i) P (X ≤ i) = ##\frac{i^2}{N^2}## and P(X < i) = 0
All of these are based on the graph

(ii) P(X = i) = P(X ≤ i) - P(X < i) = ##\frac{i^2}{N^2}##

Are my answers correct? Thanks
 
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  • #2
songoku said:
Homework Statement: Please see below
Relevant Equations: F(X) = P(X ≤ x)

View attachment 333620

I have tried to answer all the questions but I am not that sure with my answer.

View attachment 333621
That's the graph of ##F_X (x)## (I think)

(i) P (X ≤ i) = ##\frac{i^2}{N^2}## and P(X < i) = 0
All of these are based on the graph
If there are several integers between 0 and i, they have positive probability values. so P(X<i) > 0.
songoku said:
(ii) P(X = i) = P(X ≤ i) - P(X < i) = ##\frac{i^2}{N^2}##

Are my answers correct? Thanks
No. Notice that your diagram only has one i < N, but there might be several others. Also, the sum of the probabilities must equal 1, so your diagram is missing a lot of probability.
 
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  • #3
FactChecker said:
If there are several integers between 0 and i, they have positive probability values. so P(X<i) > 0.

No. Notice that your diagram only has one i < N, but there might be several others. Also, the sum of the probabilities must equal 1, so your diagram is missing a lot of probability.
Ah, I see. Now I understand the question

Revised attempt:
(i)
$$P (X ≤ i) = \frac{i^2}{N^2}$$

$$P(X < i) =
\begin{cases}
0 & \text{if } i= 0 \\
\frac{(i-1)^2}{N^2} & \text{if } i>0
\end{cases}
$$

(ii)
$$P(X = i) =
\begin{cases}
0 & \text{if } i= 0 \\
\frac{2i-1}{N^2} & \text{if } i>0
\end{cases}
$$

For P(X < i) and P(X = i), is there an answer not involving piecewise function? Thanks
 
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  • #4
just resuming your effort : using Heaviside step function H
[tex]F_X(x)=\frac{1}{N^2}\sum_{i=1}^N (i^2-(i-1)^2)H_1(x-i)[/tex]
Probability density is by differentiation
[tex]p(x)=\frac{1}{N^2}\sum_{i=1}^N (i^2-(i-1)^2)\delta(x-i)[/tex]
Probability for digits are by integrating p(x) around x=i
[tex]P(i)=\frac{i^2-(i-1)^2}{N^2}=\frac{2i-1}{N^2}[/tex]
for ##1 \leq i \leq N##. Otherwise p(x)=0, P(i)=0.
 
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  • #5
Thank you very much for the help and explanation FactChecker and anuttarasammyak
 

1. What is a cumulative distribution function (CDF)?

A cumulative distribution function (CDF) is a function that gives the probability that a random variable X will be less than or equal to a certain value x. It provides a way to describe the probability distribution of a random variable.

2. How is the CDF related to probability?

The CDF is related to probability by providing a way to calculate the probability of a random variable being less than or equal to a certain value. By using the CDF, you can determine the likelihood of a particular outcome occurring.

3. What is the difference between a probability density function (PDF) and a CDF?

A probability density function (PDF) gives the probability density at a specific value of a random variable, while a CDF gives the probability that the random variable is less than or equal to a specific value. The PDF describes the likelihood of a random variable taking on a specific value, while the CDF describes the likelihood of it being less than or equal to a specific value.

4. How can the CDF be used to calculate probabilities?

To calculate probabilities using the CDF, you can use the formula P(X ≤ x) = F(x), where P(X ≤ x) is the probability that the random variable X is less than or equal to x, and F(x) is the value of the CDF at x. By plugging in the desired value of x into the CDF function, you can determine the probability of the random variable being less than or equal to that value.

5. What is the relationship between the CDF and the survival function?

The survival function is defined as S(x) = 1 - F(x), where S(x) is the probability that the random variable X is greater than x, and F(x) is the CDF at x. The survival function provides the probability of the random variable exceeding a certain value, complementing the information provided by the CDF.

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