Cumulative Distribution function

In summary, the student was asked to find the probability of a value greater than 5 occurring, given that a value greater than 3 has already occurred. The student was unable to find the answer using the basic formula, so he was helped by his teacher. The probability of a value greater than 5 occurring is 0.365.
  • #1
boneill3
127
0

Homework Statement



I have been given a CDF of T value probabilites for t >= 0
I have been given P(t=3)=0.59 P(t=5)=0.85


Homework Equations





The Attempt at a Solution



I have been asked to find P(T > 5 | T>3 )

I was wondering how to work this out.

As this is a conditional probabilty I was heading towards

P(T > 5 | T>3 ) =
[itex]
\frac{P(T > 5 \and T>3 )}{P(t>5)}[/itex]
so the probabilty of P(T > 5 \and T>3 )} = .41
and P(t>5) = .15

But you can't use that calculation.

But wouldn't the probability of of being > 5 still just be .15

Any help would be appreciated
 
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  • #2
boneill3 said:

Homework Statement



I have been given a CDF of T value probabilites for t >= 0
I have been given P(t=3)=0.59 P(t=5)=0.85


Homework Equations





The Attempt at a Solution



I have been asked to find P(T > 5 | T>3 )

I was wondering how to work this out.

As this is a conditional probabilty I was heading towards

P(T > 5 | T>3 ) =
[itex]
\frac{P(T > 5 \and T>3 )}{P(t>5)}[/itex]
so the probabilty of P(T > 5 \and T>3 )} = .41
and P(t>5) = .15

But you can't use that calculation.

But wouldn't the probability of of being > 5 still just be .15

Any help would be appreciated
Yes, P(t> 5)= 1- .85= .15. But that is NOT "P(t>v5|P>3)"

Remember the basic formula P(A and B)= P(A|B)P(B).

Now if x> 5 then it MUST be >3 so P((x>.5) and (x>3))= P(x> 5).

P(t>5)= P(t>5|P>3)P(t> 3).

You know P(T>5) and you know P(t> 3). Put those into that formula and solve for P(t> 5|t> 3).
 
  • #3
Thanks for your help.

So we need to find


P(A|B) = P(A and B)/P(B)

we have:

P(t>5)= P(t>5|P>3)P(t> 3)

and need to find

P(t>5|P>3)= P(t>5)/P(t> 3)

P(t>5|P>3)= P(0.15)/P(0.41)

therefore

P(t>5|P>3)= 0.365

regards
 

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

A Cumulative Distribution Function (CDF) is a mathematical function that describes the probability of a random variable taking on a certain value or less. It is a way of summarizing the probability distribution of a continuous random variable.

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

A CDF represents the cumulative probability of a random variable, while a PDF represents the relative likelihood of a random variable taking on a specific value. In other words, a CDF shows how likely it is for a random variable to be less than or equal to a certain value, while a PDF shows how likely it is to take on a specific value.

3. What is the relationship between a CDF and its corresponding PDF?

A CDF can be derived from its corresponding PDF by integrating the PDF over the range of values. In other words, the CDF is the integral of the PDF. Conversely, the PDF can be obtained by differentiating the CDF.

4. How is a CDF useful in statistics and data analysis?

A CDF can be used to calculate the probability of a random variable taking on a certain value or less. It can also be used to calculate percentiles, which can help in understanding the distribution of data. Additionally, CDFs can be compared to each other to determine whether two datasets have the same underlying distribution.

5. Can a CDF be used for discrete random variables?

Yes, a CDF can be used for both continuous and discrete random variables. For discrete random variables, the CDF is a step function, with a jump at each possible value of the variable. The value of the CDF at each jump is equal to the probability of the random variable being less than or equal to that value.

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