## The Average

The average or the mean of a finite set of numbers is, well, the average. For example, the average of the numbers $\{2,3,4,4,5,7,11,12\}$ is given by: $\text{average}=\frac{2+3+4+4+5+7+11+12}{8}=\frac{48}{8}=6.$

When we have some real-valued variable (a variable with real number values), for example the heights of the students in a class, that we know all about — i.e. we have the data or statistics of the variable — we can define it’s average or mean.

### Definition

Let $x$ be a real-valued variable with data $\{x_1,x_2,\dots,x_n\}$. The average or mean of $x$, denoted by $\bar{x}$ is defined by: $\bar{x}=\frac{x_1+x_2+\cdots+x_n}{n}=\frac{\sum_{i=1}^nx_i}{n}.$

## The Expected Value

The aim of this note is to explain what the expected value is so I will just give one example of it and then from this write a definition. The expected value refers to the expected value of a real-valued random variable $X$ (I’m not sure does LC Project Maths use this term but they should). A random variable $X$ is a variable whose outputs are random. For the purposes of this piece we will assume that a random variable $X$ takes values in a finite set $\{x_1,x_2,\dots,x_n\}$. We say that $X$ takes the value $x_i$ with probability $p_i$ (I’m assuming we know the basics of probability).

### Example

Let $X$ denote the outcome of a roll of a dice. The possible values of $X$ are $1,2,3,4,5,6=\{x_1,x_2,x_3,x_4,x_5,x_6\}$. The probability of rolling a two, say, is $1/6=p_2$; and indeed $p_i=1/6$ for all $x_i$.  We calculate the expectation of $X$, $E(X)$, as $E(X)=p_1x_1+p_2x_2+p_3x_3+p_4x_4+p_5x_5+p_6x_6$ $=\frac{1}{6}(1+2+3+4+5+6)=3.5$.

In this case the expected value of $X$ is the same as the average number on the dice. Is this a coincidence.

### Definition

Suppose that $X$ is a random variable taking values in $\{x_1,x_2,\dots,x_n\}$ with probabilities $\{p_1,\dots,p_n\}$. Then the expected value of $X$ is defined by: $E(X)=p_1x_1+p_2x_2+\cdots+p_nx_n=\sum_{i=1}^np_ix_i.$

## Is there a link: Empirical probability vs a priori probabilities

### Empirical Probability

We know the statistics of a random variable — we know all the data. Suppose the data is given by $S=\{x_1,x_2,\dots,x_n\}$.  Then we can define the probability of the single event $A$ that $X=x_k$ as $P(A)=P(X=x_k)=\frac{\text{number of instances of }x_k \text{ in }S}{n}$.

That is the statistics informs the probability. $\text{STATISTICS }\rightarrow\text{ PROBABILITIES}$.

### A priori Probability

In an a priori (roughly “beforehand”) view of probability we claim that we know the probabilities without knowing any data. Good examples being coin-flipping, dice rolling, card shuffling and lottery games. Using probability we can predict what the statistics will be. For example we know that when coin flipping we will get a head about half of the time.

So in this picture we have that: $\text{PROBABILITY }\rightarrow\text{ STATISTICS}$.

In the empirical view we have the average or mean. How does the a priori picture tell us about the average? Well the expected value is a priori’s prediction of what the average will be! For expected value read expected average (i.e. if we take a series of measurements of $X$, say $x=\{x_1,x_2,\dots,x_m\}$, then $E(X)\approx\bar{x}$).

### A Justification

Starting in the empirical picture, let $x$ be a variable which is observed to take $m$ (distinct) values $\{x_1,\cdots,x_m\}$ with frequencies $\{f_1,f_2,\dots,f_m\}$ (i.e. the outcome $x_i$ occured $f_i$ times). Now the total number of measurements is $f_1+f_2+\cdots+f_m=\sum_i f_i=:N$ (i.e. $x_1$ occurs $f_1$ times, $x_2$ occurs $f_2$ times… $x_m$ occurs $f_m$ times so in total we have $\sum_i f_i$ measurements.) So in the notation of the section on empirical probability $S=\{\underbrace{x_1,x_1,\dots,x_1}_{f_1\text{ times}},\underbrace{x_2,\dots,x_2}_{f_2\text{ times}},\dots,\underbrace{x_m,\dots,x_m}_{f_m\text{ times}}\}$.

Therefore, the average of $x$ is: $\bar{x}=\frac{f_1x_1+f_2x_2+\cdots+f_nx_n}{f_1+f_2+\cdots+f_n}=\frac{\sum_i f_ix_i}{\sum_i f_i}$.

Now assign empirical probabilities to $x$ and view it as a random variable $X$ (here $N=\sum_i f_i$): $p_k=P(X=x_k)=\frac{\text{number of instances of }x_k \text{ in }S}{N}=\frac{f_i}{\sum_i f_i}$.

What about $E(X)$? Well from the definition: $E(X)=p_1x_1+p_2x_2+\cdots+p_mx_m$ $=\frac{f_1}{\sum_if_i}x_1+\frac{f_2}{\sum_if_i}x_2+\cdots+\frac{f_m}{\sum_if_i}x_m$ $=\frac{f_1x_1+\cdots+f_mx_m}{\sum_if_i}=\bar{x}$ $\bullet$

So, in conclusion, we can think of the expected value of $X$ as the expected average of $X$ were we to take a number of measurements of $X$, and take the average of these outcomes.