How Do You Convert a PDF to a CDF Using Integration?

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SUMMARY

This discussion focuses on converting a probability density function (PDF) to a cumulative distribution function (CDF) using integration techniques. The provided PDF function fy(Y) is piecewise defined, requiring integration over different intervals to derive the corresponding CDF. The integration process involves calculating areas under the curve for specified ranges, resulting in the CDF expressions for each segment of the PDF. The final CDF is defined as F(k) = P(X < k), integrating the PDF from negative infinity to k.

PREREQUISITES
  • Understanding of probability density functions (PDFs)
  • Knowledge of cumulative distribution functions (CDFs)
  • Proficiency in integration techniques, particularly for piecewise functions
  • Familiarity with the concept of limits and areas under curves
NEXT STEPS
  • Study integration techniques for piecewise functions in calculus
  • Learn about the properties and applications of cumulative distribution functions (CDFs)
  • Explore examples of converting PDFs to CDFs using different functions
  • Review statistical concepts related to probability distributions and their characteristics
USEFUL FOR

Students in statistics or mathematics, educators teaching probability theory, and data analysts working with statistical models will benefit from this discussion.

semidevil
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I"m not understanding CDF...

My textbook doesn't seem to give enough information on it.

I"m not understanding what do to when involved with CDFs in general.

So if I want to convert a pdf to cdf, given a function
fy(Y) =

0 for y <0
2y for 0 <= y <= 1/2
6 - 6y for 1/2 < y <= 1
0 for y > 1

so the book does this:
for 0 <= y <= 1/2, it integrates (0 dt from -infinite to 0) + the integeral of (2dt from 0 to y).

answer is y^2.

then, for 1/2 < y <= 1, it does the integeral of 0dt from -infinite to 0,+ the integeral of 2dt from 0 to 1/2dt, + the integeral of 6 - 6t from 1/2 to y.

answer is 6y - 3y^2 - 2.

and y > 1, = 1.

so we get the new cdf.

the book doesn't give any explanation of what it did. So what just happened here? how did they do all that just by looking at the function? why did they integrate from - infinite of 0, and then from 0 to y, and then 0 to 1/2.

well, you get my point...I don't know how to approach those problems..
 
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Given a PDF, f, the associated CDF is usually F(k):= P(X<k), which is exactly the integral from -inf to k of f. They split F into several parts in this example because the PDF is defined in several parts.
 

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