What is the difference between PDF and CDF? - ProProfs Discuss
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What is the difference between PDF and CDF?

Asked by Jasmijn , Last updated: Aug 13, 2020

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3 Answers

J. Lautner

J. Lautner

J. Lautner
J. Lautner, Product Manager, Utah

Answered Aug 12, 2020

Portable Document Format is a generic term that is generally related to Adobe PDF. Adobe PDF represents two-dimensional documents in a way that allows them to be changed independently of software package, hardware, and operating system of the application.

Each file consists of all the elements of any fixed-layout 2D document, including text, fonts, images, and 2D vector graphics. Compound Document Format is a document file format that is electronic. The World Wide Web Consortium developed it.

It was created to certify that industry members had compatibility and agreement in the inception of new standards. The Open Document Format is still the main format being promoted by the foundation as it stands.

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H. Barnes

H. Barnes

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H. Barnes
H. Barnes, Unemployed, Barnes, Atlanta

Answered Aug 04, 2020

Are you wondering what PDF and CDF are? These are concepts that are related to probability. When you say PDF, this stands for the relative frequency of failure times related to the function of time in general. When you say CDF, this is different.

This is known as a type of function. This is the function of the variable that is normally defined by a number. The CDF will be representing the cumulative values of PDF. The meanings of PDF and CDF are also different.

When you say PDF this means Probability Density Function. When you say CDF, it means Cumulative Distribution Function. A CDF function is considered to be an integral part of PDF.

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E.Evelyn

E.Evelyn

E.Evelyn
E.Evelyn

Answered Jul 31, 2020

PDF and CDF are two terms that are related to statistics. PDF stands for Probability Density Function. It uses the formula Fx(x) = d/dx(Fx(x)). It is used to define the random variable of x. CDF stands for Cumulative Distribution Function. To define this variable, a person would use the formula Fx(x) = P (X
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