Area under the curve - probability

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SUMMARY

The area under the curve of a probability density function (PDF) represents probability due to the fundamental definition of continuous random variables. Specifically, for a nonnegative function f(x), the probability P(a ≤ X ≤ b) is calculated using the integral of f(x) from a to b, denoted as P(a ≤ X ≤ b) = ∫_a^b f(x) dx. The cumulative distribution function (CDF) is defined as P(X ≤ x) = ∫_{-∞}^{x} f(t) dt, leading to the conclusion that the total area under the curve equals 1, thereby establishing the relationship between area and probability.

PREREQUISITES
  • Understanding of probability density functions (PDFs)
  • Basic knowledge of calculus, specifically integration
  • Familiarity with cumulative distribution functions (CDFs)
  • Concept of continuous random variables
NEXT STEPS
  • Study the properties of probability density functions (PDFs)
  • Learn about cumulative distribution functions (CDFs) in depth
  • Explore applications of integrals in probability theory
  • Investigate different types of distributions, such as normal and exponential distributions
USEFUL FOR

Students of statistics, mathematicians, data scientists, and anyone seeking to understand the mathematical foundations of probability and its applications in various fields.

michonamona
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It's something that I know but I've never been able to figure out 'why?' Why is does the area underneath the normal distribution (or any distribution) represent probability?

Thank you.

M
 
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michonamona said:
It's something that I know but I've never been able to figure out 'why?' Why is does the area underneath the normal distribution (or any distribution) represent probability?

Thank you.

M

The short answer to your question is the definition:

A nonnegative function f(x) is said to be the density function for the continuous random variable X if for all real numbers a < b:

P(a\le X \le b) = \int_a^b f(x)\, dx

From calculus we know the integral on the right represents the area under f(x) between a and b. Since the cumulative distribution function is given by

P(X \le x) =\int_{-\infty}^{x} f(t)\, dt

it follows that

\int_{-\infty}^{\infty} f(x)\, dx = 1

Putting these together shows why the area under the curve represents probability. Does that explanation help or did you already know that?
 

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