Cumulative Distribution Function : Example : ExamSolutions

In this example I show you how to find the cumulative distribution function from a probability density function that has several functions in. Often the cause of problems. To see this and more tutorials on continuous random variables goto ExamSolutions http://www.examsolutions.co.uk

Category:

Education

Tags:

• Cumulative
• Probability
• Distribution
• Functions
• cdf
• ExamSolutions
• Exam Solutions
• A-level
• Statistics
• maths

[julieetran]

In terms of whether using less/greater than or equal to, or less/greater than, do you just following the original f(x)? Cause I'm having trouble understanding how to know which one you should use

[ExamSolutions]

@julieetran follow the original f(x)

[sonicheroin]

is the last answer must be 1??

[ExamSolutions]

@sonicheroin yes

[choiduck]

i don't understand the last part with F(X) = 1 when x>=2???? please explain

[ExamSolutions]

@choiduck Look at it like this, suppose you have a normal fair die. What are the cances of getting a number less than or equal to 7 say. Guaranteed so the probability is 1. F(X) = 1

[rahul89dot]

so nice its was very helpful .................

[TheCornerKebab]

I understand that, kind of, but i have trouble transfering this to other questions. Do you have more? The one i'm doing now has an x on the first function too, so it's not working for this question what you're doing there.

[mrmaaza123]

Hey the video was good and really helpful, but i am having some problem in converting pmf into cdf . How to we convert these discrete functions into cdf's? hope you can help me out with this .

[TheXAER0]

why is it that the llimit is x and 1? isnt it 1 and 2 because its also =?

[The1000ankit]

@vincentcold thanks it helps,,

[vincentcold]

@The1000ankit You integrate 1/4 from 0 to x, so you would get 1/4(x-0) = 1/4x. the basic formula for CDF is integral of fx(t) dt over the range from - infinity to x where fx(t) is probability of density function. You can kindda say CDF is Fx(x) accumulates probability from the initial point up to a value x. CDF can be used to described both discrete and continuous random variables that's why it's convenient to use it.