\usepackage{listings}
\usepackage{xcolor} % used only to show the phantomed stuff
\definecolor{cas}{HTML}{e6f0fe}
+\usepackage{mathtools}
\pagestyle{fancy}
-\fancyhead[LO,LE]{Unit 3 Methods Statistics}
+\fancyhead[LO,LE]{Unit 3 Methods --- Statistics}
\fancyhead[CO,CE]{Andrew Lorimer}
\setlength\parindent{0pt}
\title{Statistics}
\author{}
\date{}
- \maketitle
+ %\maketitle
\section{Probability}
-
- \[ \Pr(A \cup B) = \Pr(A) + \Pr(B) - \Pr(A \cap B) \]
- \[ \Pr(A \cup B) = 0 \tag{mutually exclusive} \]
-
- \section{Conditional probability}
-
- \[ \Pr(A|B) = \frac{\Pr(A \cap B)}{\Pr(B)} \quad \text{where } \Pr(B) \ne 0 \]
- \[ \Pr(A) = \Pr(A|B) \cdot \Pr(B) + \Pr(A|B^{\prime}) \cdot \Pr(B^{\prime}) \tag{law of total probability} \]
-
- \[ \Pr(A \cap B) = \Pr(A|B) \times \Pr(B) \tag{multiplication theorem} \]
+ \subsection*{Probability theorems}
- For independent events:
+ \begin{align*}
+ \textbf{Union:} &&\Pr(A \cup B) &= \Pr(A) + \Pr(B) - \Pr(A \cap B) \\
+ \textbf{Multiplication theorem:} &&\Pr(A \cap B) &= \Pr(A|B) \times \Pr(B) \\
+ \textbf{Conditional:} &&\Pr(A|B) &= \frac{\Pr(A \cap B)}{\Pr(B)} \\
+ \textbf{Law of total probability:} &&\Pr(A) &= \Pr(A|B) \cdot \Pr(B) + \Pr(A|B^{\prime}) \cdot \Pr(B^{\prime}) \\
+ \end{align*}
+
+ Mutually exclusive \(\implies \Pr(A \cup B) = 0\) \\
- \begin{itemize}
- \item \(\Pr(A \cap B) = \Pr(A) \times \Pr(B)\)
- \item \(\Pr(A|B) = \Pr(A)\)
- \item \(\Pr(B|A) = \Pr(B)\)
- \end{itemize}
+ Independent events:
+ \begin{flalign*}
+ \quad \Pr(A \cap B) &= \Pr(A) \times \Pr(B)& \\
+ \Pr(A|B) &= \Pr(A) \\
+ \Pr(B|A) &= \Pr(B)
+ \end{flalign*}
- \subsection{Discrete random distributions}
+ \subsection*{Discrete random distributions}
Any experiment or activity involving chance will have a probability associated with each result or \textit{outcome}. If the outcomes have a reference to \textbf{discrete numeric values} (outcomes that can be counted), and the result is unknown, then the activity is a \textit{discrete random probability distribution}.
- \subsubsection{Discrete probability distributions}
+ \subsubsection*{Discrete probability distributions}
If an activity has outcomes whose probability values are all positive and less than one ($\implies 0 \le p(x) \le 1$), and for which the sum of all outcome probabilities is unity ($\implies \sum p(x) = 1$), then it is called a \textit{probability distribution} or \textit{probability mass} function.
\item \textbf{Probability distribution graph} - a series of points on a cartesian axis representing results of outcomes. $\Pr(X=x)$ is on $y$-axis, $x$ is on $x$ axis.
\item \textbf{Mean $\mu$} or \textbf{expected value} \(E(X)\) - measure of central tendency. Also known as \textit{balance point}. Centre of a symmetrical distribution.
\begin{align*}
- \overline{x} = \mu = E(X) &= \frac{\Sigma(xf)}{\Sigma(f)} \\
- &= \sum_{i=1}^n (x_i \cdot P(X=x_i)) \\
- &= \int_{-\infty}^{\infty} x\cdot f(x) \> dx \quad \text{(for pdf } f \text{)}
- &= \sum_{-\infty}^{\infty}
+ \overline{x} = \mu = E(X) &= \frac{\Sigma \left[ x \cdot f(x) \right]}{\Sigma f} \tag{where \(f =\) absolute frequency} \\
+ &= \sum_{i=1}^n \left[ x_i \cdot \Pr(X=x_i) \right] \tag{for \(n\) values of \(x\)}\\
+ &= \int_{-\infty}^{\infty} (x\cdot f(x)) \> dx \tag{for pdf \(f\)}
\end{align*}
\item \textbf{Mode} - most popular value (has highest probability of \(X\) values). Multiple modes can exist if \(>1 \> X\) value have equal-highest probability. Number must exist in distribution.
- \item \textbf{Median \(m\)} - the value of \(x\) such that \(\Pr(X \le m) = \Pr(X \ge m) = 0.5\). If \(m > 0.5\), then value of \(X\) that is reached is the median of \(X\). If \(m = 0.5 = 0.5\), then \(m\) is halfway between this value and the next.
+ \item \textbf{Median \(m\)} - the value of \(x\) such that \(\Pr(X \le m) = \Pr(X \ge m) = 0.5\). If \(m > 0.5\), then value of \(X\) that is reached is the median of \(X\). If \(m = 0.5 = 0.5\), then \(m\) is halfway between this value and the next. To find \(m\), add values of \(X\) from smallest to alrgest until the sum reaches 0.5.
\[ m = X \> \text{such that} \> \int_{-\infty}^{m} f(x) dx = 0.5 \]
\item \textbf{Variance $\sigma^2$} - measure of spread of data around the mean. Not the same magnitude as the original data. For distribution \(x_1 \mapsto p_1, x_2 \mapsto p_2, \dots, x_n \mapsto p_n\):
\begin{align*}
\sigma^2=\operatorname{Var}(x) &= \sum_{i=1}^n p_i (x_i-\mu)^2 \\
&= \sum (x-\mu)^2 \times \Pr(X=x) \\
- &= \sum x^2 \times p(x) - \mu^2
+ &= \sum x^2 \times p(x) - \mu^2 \\
+ &= \operatorname{E}(X^2) - [\operatorname{E}(X)]^2
+ \end{align*}
+ \item \textbf{Standard deviation $\sigma$} - measure of spread in the original magnitude of the data. Found by taking square root of the variance:
+ \begin{align*}
+ \sigma &= \operatorname{sd}(X) \\
+ &= \sqrt{\operatorname{Var}(X)}
\end{align*}
- \item \textbf{Standard deviation $\sigma$} - measure of spread in the original magnitude of the data. Found by taking square root of the variance: $\sigma =\operatorname{sd}(X)=\sqrt{\operatorname{Var}(X)}$
\end{itemize}
- \subsubsection{Expectation theorems}
+ \subsubsection*{Expectation theorems}
+
+ For some non-linear function \(g\), the expected value \(E(g(X))\) is not equal to \(g(E(X))\).
\begin{align*}
- E(aX \pm b) &= aE(X) \pm b \\
- E(z) &= z \\
- E(X+Y) &= E(X) + E(Y) \\
- E(X)^n &= \Sigma x^n \cdot p(x) \\
- &\ne [E(X)]^2
+ E(X^n) &= \Sigma x^n \cdot p(x) \tag{non-linear function} \\
+ &\ne [E(X)]^n \\
+ E(aX \pm b) &= aE(X) \pm b \tag{linear function} \\
+ E(b) &= b \tag{for constant \(b \in \mathbb{R}\)}\\
+ E(X+Y) &= E(X) + E(Y) \tag{for two random variables}
\end{align*}
+ \subsubsection*{Variance theorems}
+
+ \[ \operatorname{Var}(aX \pm bY \pm c) = a^2 \operatorname{Var}(X) + b^2 \operatorname{Var}(Y) \]
\section{Binomial Theorem}
&= \sum_{k=0}^n {n \choose k} x^k y^{n-k}
\end{align*}
+ \subsubsection*{Patterns}
\begin{enumerate}
\item powers of \(x\) decrease \(n \rightarrow 0\)
\item powers of \(y\) increase \(0 \rightarrow n\)
\item Number of terms in \((x+a)^n\) expanded \& simplified is \(n+1\)
\end{enumerate}
- Combinations: \(^n\text{C}_r = {N\choose k}\) (binomial coefficient)
+ \subsubsection*{Combinatorics}
+
+ \[ \text{Binomial coefficient:} \quad ^n\text{C}_r = {N\choose k} \]
+
\begin{itemize}
\item Arrangements \({n \choose k} = \frac{n!}{(n-r)}\)
\item Combinations \({n \choose k} = \frac{n!}{r!(n-r)!}\)
\item Note \({n \choose k} = {n \choose k-1}\)
\end{itemize}
- \subsubsection{Pascal's Triangle}
+ \colorbox{cas}{On CAS:} (soft keyboard) \keystroke{\(\downarrow\)} \(\rightarrow\) \keystroke{Advanced} \(\rightarrow\) \verb;nCr(n,cr);
+
+ \subsubsection*{Pascal's Triangle}
\begin{tabular}{>{$}l<{$\hspace{12pt}}*{13}{c}}
n=\cr0&&&&&&&1&&&&&&\\
6&1&&6&&15&&20&&15&&6&&1
\end{tabular}
- \colorbox{cas}{On CAS:} (soft keys) \keystroke{\(\downarrow\)} \(\rightarrow\) \keystroke{Advanced} \(\rightarrow\) \verb;nCr(n,cr);
-
\section{Binomial distributions}
(aka Bernoulli distributions)
\begin{align*}
- \Pr(X=x) &= {n \choose x} p^x (1-p)^{n-x} \\
+ \text{Defined by} \quad X &\sim \operatorname{Bi}(n,p) \\
+ \implies \Pr(X=x) &= {n \choose x} p^x (1-p)^{n-x} \\
&= {n \choose x} p^x q^{n-x}
\end{align*}
+ where:
+ \begin{description}
+ \item \(n\) is the number of trials
+ \item There are two possible outcomes: \(S\) or \(F\)
+ \item \(\Pr(\text{success}) = p\)
+ \item \(\Pr(\text{failure}) = 1-p = q\)
+ \end{description}
+
+ \subsection*{Conditions for a binomial variable/distribution}
\begin{enumerate}
\item Two possible outcomes: \textbf{success} or \textbf{failure}
\item \(\Pr(\text{success})\) is constant across trials (also denoted \(p\))
\item Finite number \(n\) of independent trials
\end{enumerate}
- If these conditions are met, then it is a Binomial Random Variable. This variable is said to have a \textit{binomial probability distribution}.
+ \subsection*{\colorbox{cas}{Solve on CAS}}
+
+ Main \(\rightarrow\) Interactive \(\rightarrow\) Distribution \(\rightarrow\) \verb;binomialPDf;
+
+ \hspace{2em} Input \verb;x; (no. of successes), \verb;numtrial; (no. of trials), \verb;pos; (probbability of success)
+
+ \subsection*{Properties of \(X \sim \operatorname{Bi}(n,p)\)}
+
+ \begin{align*}
+ \textbf{Mean} \hspace{-4cm} &&\mu(X) &= np \\
+ \textbf{Variance} \hspace{-4cm} &&\sigma^2(X) &= np(1-p) \\
+ \textbf{s.d.} \hspace{-4cm} &&\sigma(X) &= \sqrt{np(1-p)}
+ \end{align*}
+
+ \subsection*{Applications of binomial distributions}
+
+ \[ \Pr(X \ge a) = 1 - \Pr(X < a) \]
+
+ \section{Continuous probability}
+
+ \subsection*{Continuous random variables}
\begin{itemize}
- \item \(n\) is the number of trials
- \item There are two possible outcomes: \(S\) or \(F\)
- \item \(\Pr(\text{success}) = p\)
- \item \(\Pr(\text{failure}) = 1-p = q\)
- \item Shorthand notation: \(X \sim \operatorname{Bi}(n,p)\)
+ \item a variable that can take any real value in an interval
+ \end{itemize}
+
+ \subsection*{Probability density functions}
+
+ \begin{itemize}
+ \item area under curve \( = 1 \implies \int f(x) \> dx = 1\)
+ \item \(f(x) \ge 0 \forall x\)
+ \item pdfs may be linear
+ \item must show sections where \(f(x) = 0\) (use open/closed circles)
\end{itemize}
- \colorbox{cas}{On CAS:} Main \(\rightarrow\) Interactive \(\rightarrow\) Distribution \(\rightarrow\) \verb;binomialPDf; \\
- Input \verb;x; (no. of successes), \verb;numtrial; (no. of trials), \verb;pos; (probbability of success)
+ \[ Pr(a \le X \le b) = \int^b_a f(x) \> dx \]
+
+ \colorbox{cas}{On CAS:} Interactive \(\rightarrow\) Distribution \(\rightarrow\) \verb;normCdf;.
+
+ For function in domain \(a \le x \le b\):
+
+ \[ \operatorname{E}(X) = \int^b_a x f(x) \> dx \]
+
+ \[ \operatorname{sd}(X) = \sqrt{\operatorname{Var}(X)} = \sqrt{\operatorname{E}(X^2)-[\operatorname{E}(X)]^2} \]
\end{document}