logseq_notes/pages/hls__section_2.2_1686907456219_0.md

file:: 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
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• 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
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