logseq_notes/pages/hls__section_2.2_1686907456219_0.md

file:: [section_2.2_1686907456219_0.pdf](../assets/section_2.2_1686907456219_0.pdf)
file-path:: ../assets/section_2.2_1686907456219_0.pdf

- The Definition of Distribution Function
  ls-type:: annotation
  hl-page:: 3
  hl-color:: blue
  id:: 648c2a50-7acf-4cd5-b075-d0e970e114a4
- The Properties of Distribution Function
  ls-type:: annotation
  hl-page:: 4
  hl-color:: blue
  id:: 648c2a73-3961-4723-90e8-5f160bb18e0d
- [:span]
  ls-type:: annotation
  hl-page:: 4
  hl-color:: yellow
  id:: 648c2a88-3d0f-47d2-be44-21df002a1def
  hl-type:: area
  hl-stamp:: 1686907527770
- [:span]
  ls-type:: annotation
  hl-page:: 7
  hl-color:: yellow
  id:: 648c2abf-5c5e-4af9-9e13-bb566f3206e8
  hl-type:: area
  hl-stamp:: 1686907580838
- [:span]
  ls-type:: annotation
  hl-page:: 9
  hl-color:: yellow
  id:: 648c2b26-e92d-43f7-b8e1-60b51a2b5268
  hl-type:: area
  hl-stamp:: 1686907685844
- A random variable is said to be of discrete type if the number of different values it can take is finite or countably infinite.
  ls-type:: annotation
  hl-page:: 17
  hl-color:: yellow
  id:: 648c2be0-787e-4e1f-8d0c-b859f0d08383
  hl-stamp:: 1686907879808
- We call X a continuous random variable if there is a function f defined for all x ∈ R and having the following properties:
  ls-type:: annotation
  hl-page:: 25
  hl-color:: yellow
  id:: 648c2c54-b536-40ba-8191-ef1aeb2be0b9
- The Distribution Function of Function of a Random Variable
  ls-type:: annotation
  hl-page:: 35
  hl-color:: blue
  id:: 648c2c9a-fcca-43e3-94a6-466f52131b48
- we can assert that if X is a random variable, then Y := g(X) = g(X(ω)), where g is a real-valued function defined on the real line, is a random variable as well
  ls-type:: annotation
  hl-page:: 35
  hl-color:: yellow
  id:: 648c2d05-82d4-472f-901c-b362852e2a3a
- [:span]
  ls-type:: annotation
  hl-page:: 40
  hl-color:: yellow
  id:: 648c2de1-d9a7-4760-bb4f-beec4b41102c
  hl-type:: area
  hl-stamp:: 1686908384666