1\documentclass[a4paper]{article} 2\usepackage[a4paper,margin=2cm]{geometry} 3\usepackage{array} 4\usepackage{amsmath} 5\usepackage{amssymb} 6\usepackage{tcolorbox} 7\usepackage{fancyhdr} 8\usepackage{pgfplots} 9\usepackage{tabularx} 10\usepackage{keystroke} 11\usepackage{listings} 12\usepackage{xcolor} % used only to show the phantomed stuff 13\definecolor{cas}{HTML}{e6f0fe} 14\usepackage{mathtools} 15 16\pagestyle{fancy} 17\fancyhead[LO,LE]{Unit 3 Methods --- Statistics} 18\fancyhead[CO,CE]{Andrew Lorimer} 19 20\setlength\parindent{0pt} 21 22\begin{document} 23 24 \title{Statistics} 25 \author{} 26 \date{} 27 %\maketitle 28 29 \section{Probability} 30 31 \subsection*{Probability theorems} 32 33 \begin{align*} 34 \textbf{Union:} &&\Pr(A \cup B) &= \Pr(A) + \Pr(B) - \Pr(A \cap B) \\ 35 \textbf{Multiplication theorem:} &&\Pr(A \cap B) &= \Pr(A|B) \times \Pr(B) \\ 36 \textbf{Conditional:} &&\Pr(A|B) &= \frac{\Pr(A \cap B)}{\Pr(B)} \\ 37 \textbf{Law of total probability:} &&\Pr(A) &= \Pr(A|B) \cdot \Pr(B) + \Pr(A|B^{\prime}) \cdot \Pr(B^{\prime}) \\ 38 \end{align*} 39 40 Mutually exclusive \(\implies \Pr(A \cup B) = 0\) \\ 41 42 Independent events: 43 \begin{flalign*} 44 \quad \Pr(A \cap B) &= \Pr(A) \times \Pr(B)& \\ 45 \Pr(A|B) &= \Pr(A) \\ 46 \Pr(B|A) &= \Pr(B) 47 \end{flalign*} 48 49 \subsection*{Discrete random distributions} 50 51 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}. 52 53 \subsubsection*{Discrete probability distributions} 54 55 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. 56 57 \begin{itemize} 58 \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. 59 \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. 60 \begin{align*} 61 \overline{x} = \mu = E(X) &= \frac{\Sigma \left[ x \cdot f(x) \right]}{\Sigma f} \tag{where \(f =\) absolute frequency} \\ 62 &= \sum_{i=1}^n \left[ x_i \cdot \Pr(X=x_i) \right] \tag{for \(n\) values of \(x\)}\\ 63 &= \int_{-\infty}^{\infty} (x\cdot f(x)) \> dx \tag{for pdf \(f\)} 64 \end{align*} 65 \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. 66 \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. 67 \[ m = X \> \text{such that} \> \int_{-\infty}^{m} f(x) dx = 0.5 \] 68 \item \textbf{Variance $\sigma^2$} - measure of spread of data around the mean. Not the same magnitude as the original data. For distribution \(x_1 \mapsto p_1, x_2 \mapsto p_2, \dots, x_n \mapsto p_n\): 69 \begin{align*} 70 \sigma^2=\operatorname{Var}(x) &= \sum_{i=1}^n p_i (x_i-\mu)^2 \\ 71 &= \sum (x-\mu)^2 \times \Pr(X=x) \\ 72 &= \sum x^2 \times p(x) - \mu^2 \\ 73 &= \operatorname{E}(X^2) - [\operatorname{E}(X)]^2 74 \end{align*} 75 \item \textbf{Standard deviation $\sigma$} - measure of spread in the original magnitude of the data. Found by taking square root of the variance: 76 \begin{align*} 77 \sigma &= \operatorname{sd}(X) \\ 78 &= \sqrt{\operatorname{Var}(X)} 79 \end{align*} 80 \end{itemize} 81 82 \subsubsection*{Expectation theorems} 83 84 For some non-linear function \(g\), the expected value \(E(g(X))\) is not equal to \(g(E(X))\). 85 86 \begin{align*} 87 E(X^n) &= \Sigma x^n \cdot p(x) \tag{non-linear function} \\ 88 &\ne [E(X)]^n \\ 89 E(aX \pm b) &= aE(X) \pm b \tag{linear function} \\ 90 E(b) &= b \tag{for constant \(b \in \mathbb{R}\)}\\ 91 E(X+Y) &= E(X) + E(Y) \tag{for two random variables} 92 \end{align*} 93 94 \subsubsection*{Variance theorems} 95 96 \[ \operatorname{Var}(aX \pm bY \pm c) = a^2 \operatorname{Var}(X) + b^2 \operatorname{Var}(Y) \] 97 98 \section{Binomial Theorem} 99 100 \begin{align*} 101 (x+y)^n &= {n \choose 0} x^n y^0 + {n \choose 1} x^{n-1}y^1 + {n \choose 2} x^{n-2}y^2 + \dots + {n \choose n-1}x^1 y^{n-1} + {n \choose n} x^0 y^n \\ 102 &= \sum_{k=0}^n {n \choose k} x^{n-k} y^k \\ 103 &= \sum_{k=0}^n {n \choose k} x^k y^{n-k} 104 \end{align*} 105 106 \subsubsection*{Patterns} 107 \begin{enumerate} 108 \item powers of \(x\) decrease \(n \rightarrow 0\) 109 \item powers of \(y\) increase \(0 \rightarrow n\) 110 \item coefficients are given by \(n\)th row of Pascal's Triangle where \(n=0\) has one term 111 \item Number of terms in \((x+a)^n\) expanded \& simplified is \(n+1\) 112 \end{enumerate} 113 114 \subsubsection*{Combinatorics} 115 116 \[ \text{Binomial coefficient:} \quad ^n\text{C}_r = {N\choose k} \] 117 118 \begin{itemize} 119 \item Arrangements \({n \choose k} = \frac{n!}{(n-r)}\) 120 \item Combinations \({n \choose k} = \frac{n!}{r!(n-r)!}\) 121 \item Note \({n \choose k} = {n \choose k-1}\) 122 \end{itemize} 123 124 \colorbox{cas}{On CAS:} (soft keyboard) \keystroke{\(\downarrow\)} \(\rightarrow\) \keystroke{Advanced} \(\rightarrow\) \verb;nCr(n,cr); 125 126 \subsubsection*{Pascal's Triangle} 127 128 \begin{tabular}{>{$}l<{$\hspace{12pt}}*{13}{c}} 129 n=\cr0&&&&&&&1&&&&&&\\ 130 1&&&&&&1&&1&&&&&\\ 131 2&&&&&1&&2&&1&&&&\\ 132 3&&&&1&&3&&3&&1&&&\\ 133 4&&&1&&4&&6&&4&&1&&\\ 134 5&&1&&5&&10&&10&&5&&1&\\ 135 6&1&&6&&15&&20&&15&&6&&1 136 \end{tabular} 137 138 \section{Binomial distributions} 139 140 (aka Bernoulli distributions) 141 142 \begin{align*} 143 \text{Defined by} \quad X &\sim \operatorname{Bi}(n,p) \\ 144 \implies \Pr(X=x) &= {n \choose x} p^x (1-p)^{n-x} \\ 145 &= {n \choose x} p^x q^{n-x} 146 \end{align*} 147 148 where: 149 \begin{description} 150 \item \(n\) is the number of trials 151 \item There are two possible outcomes: \(S\) or \(F\) 152 \item \(\Pr(\text{success}) = p\) 153 \item \(\Pr(\text{failure}) = 1-p = q\) 154 \end{description} 155 156 \subsection*{Conditions for a binomial variable/distribution} 157 \begin{enumerate} 158 \item Two possible outcomes: \textbf{success} or \textbf{failure} 159 \item \(\Pr(\text{success})\) is constant across trials (also denoted \(p\)) 160 \item Finite number \(n\) of independent trials 161 \end{enumerate} 162 163 \subsection*{\colorbox{cas}{Solve on CAS}} 164 165 Main \(\rightarrow\) Interactive \(\rightarrow\) Distribution \(\rightarrow\) \verb;binomialPDf; 166 167 \hspace{2em} Input \verb;x; (no. of successes), \verb;numtrial; (no. of trials), \verb;pos; (probbability of success) 168 169 \subsection*{Properties of \(X \sim \operatorname{Bi}(n,p)\)} 170 171 \begin{align*} 172 \textbf{Mean} \hspace{-4cm} &&\mu(X) &= np \\ 173 \textbf{Variance} \hspace{-4cm} &&\sigma^2(X) &= np(1-p) \\ 174 \textbf{s.d.} \hspace{-4cm} &&\sigma(X) &= \sqrt{np(1-p)} 175 \end{align*} 176 177 \subsection*{Applications of binomial distributions} 178 179 \[ \Pr(X \ge a) = 1 - \Pr(X < a) \] 180 181 \section{Continuous probability} 182 183 \subsection*{Continuous random variables} 184 185 \begin{itemize} 186 \item a variable that can take any real value in an interval 187 \end{itemize} 188 189 \subsection*{Probability density functions} 190 191 \begin{itemize} 192 \item area under curve \( = 1 \implies \int f(x) \> dx = 1\) 193 \item \(f(x) \ge 0 \forall x\) 194 \item pdfs may be linear 195 \item must show sections where \(f(x) = 0\) (use open/closed circles) 196 \end{itemize} 197 198 \[ Pr(a \le X \le b) = \int^b_a f(x) \> dx \] 199 200 \colorbox{cas}{On CAS:} Interactive \(\rightarrow\) Distribution \(\rightarrow\) \verb;normCdf;. 201 202 For function in domain \(a \le x \le b\): 203 204 \[ \operatorname{E}(X) = \int^b_a x f(x) \> dx \] 205 206 \[ \operatorname{sd}(X) = \sqrt{\operatorname{Var}(X)} = \sqrt{\oepratorname{E}(X^2)-[\operatorname{E}(X)]^2} \] 207 208\end{document}