Stats/prob: finding cumulative distribution function

In summary: We are evaluating a definite integral from 1 to x, so there is no constant of integration. In the "indefinite" integral that you usually see in a calculus course, there is a constant of integration because we are finding a set of functions whose derivative is the given function f(x). But here, we are evaluating a definite integral, not finding a set of functions, so there's no constant of integration.In summary, The CDF for the given pdf is F(x) = 0 for x ≤ 0, F(x) = 1 for x ≥ 2, and F(x) = (2/3)x - (1/3) for 0 < x ≤ 1. The last part of the
  • #1
Phox
37
0

Homework Statement



given pdf:

f(x) = 2/3x for 0<=x<=1
f(x) = 2/3 for 1<x<=2
f(x) = 0 elsewhere

Find the CDF.


Homework Equations





The Attempt at a Solution



I've found:

F(x) = 0 for x<= 0
F(x) = 1 for x>=2
F(x) = (1/3)x2 for 0<=x<=1

and I found:

F(x) = (2/3)x for 1<x<=2

However, the last bit is incorrect. It should be F(x) = (2/3)x -(1/3)

I'm unclear as to why. I think it has something to do with solving for the constant of integration, but I'm not sure exactly.
 
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  • #2
Yes, it has to do with the integration constant. Your CDF has to be continuous, so you need to fix that constant value so that

$$\lim_{\epsilon \rightarrow 0} F(1 - \epsilon) = \lim_{\epsilon \rightarrow 0} F(1 + \epsilon).$$

That is, the limit of the CDF on either side of x = 1 have to be the same, which wouldn't be true if you didn't fix the constant of integration to be -1/3 just above x=1.
 
  • #3
Mute said:
Yes, it has to do with the integration constant. Your CDF has to be continuous, so you need to fix that constant value so that

$$\lim_{\epsilon \rightarrow 0} F(1 - \epsilon) = \lim_{\epsilon \rightarrow 0} F(1 + \epsilon).$$

That is, the limit of the CDF on either side of x = 1 have to be the same, which wouldn't be true if you didn't fix the constant of integration to be -1/3 just above x=1.

Could you clarify.. what is epsilon?
 
  • #4
Phox said:

Homework Statement



given pdf:

f(x) = 2/3x for 0<=x<=1
f(x) = 2/3 for 1<x<=2
f(x) = 0 elsewhere

Find the CDF.


Homework Equations





The Attempt at a Solution



I've found:

F(x) = 0 for x<= 0
F(x) = 1 for x>=2
F(x) = (1/3)x2 for 0<=x<=1

and I found:

F(x) = (2/3)x for 1<x<=2

However, the last bit is incorrect. It should be F(x) = (2/3)x -(1/3)

I'm unclear as to why. I think it has something to do with solving for the constant of integration, but I'm not sure exactly.

You have F correct for x ≤ 1 and for x ≥ 2. Since the random variable has a finite density function, its F(x) must be a continuous function, and since F'(x) = 2/3 on [1,2], F must increase linearly with slope 2/3, starting from F(1) = 1/3 and ending at F(2) = 1. You can figure out what the formula must be for F(x) in the region 1 ≤ x ≤ 2.

Basically, you need to use
[tex] F(x) = F(1) + \int_{1}^{x} f(t) \, dt, \: 1 \leq x \leq 2.[/tex]
Note that in this calculation there is NO constant of integration!
 

Related to Stats/prob: finding cumulative distribution function

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

A cumulative distribution function (CDF) is a mathematical function that shows the probability that a random variable takes on a value less than or equal to a given value. It is used to describe the probability distribution of a continuous random variable.

2. How is a CDF different from a probability density function (PDF)?

A CDF gives the cumulative probability of a random variable taking on a value less than or equal to a given value, while a PDF gives the probability density at a specific value. In other words, a CDF is the integral of the PDF.

3. How do you calculate the CDF for a given data set?

To calculate the CDF for a given data set, you first need to sort the data in ascending order. Then, for each data point, divide the number of data points that are less than or equal to that value by the total number of data points. This will give you the cumulative probability for each data point.

4. What is the range of values that a CDF can take on?

A CDF can take on values between 0 and 1, as it represents the probability of a random variable taking on a value less than or equal to a given value. The CDF will approach 0 for very small values and approach 1 for very large values.

5. How can a CDF be used in hypothesis testing?

In hypothesis testing, the CDF can be used to determine the p-value, which is the probability of obtaining a result at least as extreme as the one observed, assuming the null hypothesis is true. The p-value is calculated by finding the area under the curve of the CDF that corresponds to the test statistic. If the p-value is less than the significance level, the null hypothesis can be rejected.

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