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Mark 2024-10-03 10:37:30 -07:00
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2 changed files with 49 additions and 3 deletions

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@ -60,6 +60,9 @@ As a function, $\mathcal{H}$ maps values in $\Omega$ to values in $\mathbb{Z}^+_
\item ...and so on. \item ...and so on.
\end{itemize} \end{itemize}
Intuitively, a random variable assigns a \say{value} in $\mathbb{R}$ to every possible outcome.
\definition{} \definition{}
We can compute the probability that a random variable takes a certain value by computing the probability of We can compute the probability that a random variable takes a certain value by computing the probability of
the set of outcomes that produce that value. \par the set of outcomes that produce that value. \par
@ -92,18 +95,18 @@ Find $\mathcal{P}(\mathcal{X} = x)$ for all $x$ in $\mathbb{Z}$.
% %
\definition{} \definition{}<defexp>
Say we have a random variable $\mathcal{X}$ that produces outputs in $\mathbb{R}$. \par Say we have a random variable $\mathcal{X}$ that produces outputs in $\mathbb{R}$. \par
The \textit{expected value} of $\mathcal{X}$ is then defined as The \textit{expected value} of $\mathcal{X}$ is then defined as
\begin{equation*} \begin{equation*}
\mathcal{E}(\mathcal{X}) \mathcal{E}(\mathcal{X})
~\coloneqq~ \sum_{x \in A}\Bigl(x \times \mathcal{P}\bigl(\mathcal{X} = x\bigr)\Bigr) ~\coloneqq~ \sum_{x \in \mathbb{R}}\Bigl(x \times \mathcal{P}\bigl(\mathcal{X} = x\bigr)\Bigr)
~=~ \sum_{\omega \in \Omega}\Bigl(\mathcal{X}(\omega) \times \mathcal{P}(\omega)\Bigr) ~=~ \sum_{\omega \in \Omega}\Bigl(\mathcal{X}(\omega) \times \mathcal{P}(\omega)\Bigr)
\end{equation*} \end{equation*}
That is, $\mathcal{E}(\mathcal{X})$ is the average of all possible outputs of $\mathcal{X}$ weighted by their probability. That is, $\mathcal{E}(\mathcal{X})$ is the average of all possible outputs of $\mathcal{X}$ weighted by their probability.
\problem{} \problem{}
Say we flip a coin with $\mathcal{P}(\texttt{H}) = \nicefrac{1}{3}$ three times. \par Say we flip a coin with $\mathcal{P}(\texttt{H}) = \nicefrac{1}{3}$ two times. \par
Define $\mathcal{H}$ as the number of heads we see. \par Define $\mathcal{H}$ as the number of heads we see. \par
Find $\mathcal{E}(\mathcal{H})$. Find $\mathcal{E}(\mathcal{H})$.
@ -113,6 +116,14 @@ Find $\mathcal{E}(\mathcal{H})$.
Let $\mathcal{A}$ and $\mathcal{B}$ be two random variables. \par Let $\mathcal{A}$ and $\mathcal{B}$ be two random variables. \par
Show that $\mathcal{E}(\mathcal{A} + \mathcal{B}) = \mathcal{E}(\mathcal{A}) + \mathcal{E}(\mathcal{B})$. Show that $\mathcal{E}(\mathcal{A} + \mathcal{B}) = \mathcal{E}(\mathcal{A}) + \mathcal{E}(\mathcal{B})$.
\begin{solution}
Use the second definition of $\mathcal{E}$, $\sum_{\omega \in \Omega}\Bigl(\mathcal{X}(\omega) \times \mathcal{P}(\omega)\Bigr)$.
\vspace{2mm}
Make sure students understand all parts of \ref{defexp}, and are comfortable with the fact that a random variable \say{assigns values} to outcomes.
\end{solution}
\vfill \vfill
\definition{} \definition{}

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resources/share/main.tex Executable file
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@ -0,0 +1,35 @@
% use [nosolutions] flag to hide solutions.
% use [solutions] flag to show solutions.
\documentclass[
solutions,
singlenumbering
]{./ormc_handout}
\title{The Size of Sets}
\subtitle{Prepared by Mark on \today{}}
\begin{document}
\maketitle
\section{Set Basics}
\definition{}
A \textit{set} is a collection of objects. \par
If $a$ is an element of set $S$, we write $a \in S$. This is pronounced \say{$a$ in $S$.} \par
The position of each element in a set or the number of times it is repeated doesn't matter. \par
All that matters is \textit{which} elements are in the set.
\vspace{2mm}
We say two sets $A$ and $B$ are equal if every element of $A$ is in $B$, and every element of $B$ is in $A$. This is known as the \textit{principle of extensionality.}
\problem{}
Convince yourself that $\{a, b\} = \{b, a\} = \{a, b, a, b, b\}$.
\begin{solution}
This is a solution.
\end{solution}
\end{document}