Binomial Distribution

Binomial Distribution

A binomial distribution is a Discrete Distribution of Random Variables

Intuition

Assume there are $n$ independent experiments

• an event $A$ can either appear with probability $p$ or not appear with probability $q = 1 -p$
• an RV $X$ is the number of experiments in which $A$ appeared
• such experiments are called “Bernoulli Trials”

Using Bernoulli Formula, can calculate that

• the probability of $A$ happening $k$ times out of $n$ trials is
• $P_n(k) = C_n^k \ p^k q^{n-k}$, $0 \leqslant k \leqslant n$

Example

$X \sim \text{Bin}(10, 0.5)$

Main Properties

Expected Value

$E[X] = np$

• let $X$ be the # of independent experiments in which $A$ appeared
  • $X = X_1 + X_2 + … + X_n$,
  • where $X_i = 1$ if $A$ happened in experiment $i$, and $X_i = 0$ otherwise
• $E[X_i] = 1^2 \cdot p + 0^2 \cdot q = P(A)$
• $E[X] = E \left[ \sum X_i \right] = \sum E[X_i] = np$

Variance

’'’Thm’’’ $\text{Var}[X] = npq$

’'’Proof’’’

• let $X$ be the # of independent experiments in which $A$ appeared,
  • $X = X_1 + X_2 + … + X_n$
• $X_i$ are pair-wise independent, i.e. the outcome of one experiment doesn’t depend on the outcome of any other experiment
• thus,
  • $\text{Var}[X] = \sum_{i = 1}^n \text{Var}(X_i)$
  • $E[X_i] = P(A)$
  • $\text{Var}[X_i] = E[X_i^2] - E^2[X_i] = p - p^2 = p(1 - p) = pq$
• since there are $n$ $\text{Var}[X_i]$ then we multiply it by $n$:
• $\text{Var}[X] = npq$

We say that $X$ follows the Binomial Distribution

Examples

Example 1

Произведено 10 независимых испытаний, в каждом из которых вероятность появления события равна 0.6. Найти дисперсию С.В. $X$ - числа появления события в этих испытаниях

• $n = 10, p = 0.6, q = 1 - 0.6 = 0.4 $
• $\text{Var}[X] = npq = 10 \cdot 0.6 \cdot 0.4 = 2.4$

Example 2

Монета брошена два раза. Написать в виде таблицы закон распределения случайной величины $X$ - число выпаданий герба.

$p = 0.5, q = 0.5$

Возможные значения: $x_0 = 0, x_1 = 1, x_2 = 2$

• $P_2(2) = C_2^2 p^2 = 0.25$
• $P_2(1) = C_2^1 pq = 0.5$
• $P_2(0) = C_2^0 q^2 = 0.25$

Normal Approximation

The Bernoulli formula is cumbersome when $n$ is large

• in some cases, the Normal Distribution is a fast way of estimating the binomial probabilities

Binomial Coin Experiment

Link: http://socr.stat.ucla.edu/htmls/SOCR_Experiments.html

• go to the applet page and select “Binomial Coin Experiment”
• set # of trials to 20 and prob of success to 0.13
  • we see the theoretical shape
  • the applet allows to simulate coin flips and we see that the empirical distribution approaches the theoretical
• what’s the $n$ when we can obtain a unimodal and symmetric distribution?

We see that around $n = $ 50-60 it becomes quite symmetric

It’s reasonable to use the Normal Distribution to approximate Binomial

• parameters: $\mu = np, \sigma = \sqrt{npq}$
• note: the sample size should be sufficiently large:
  • both $n \cdot p$ and $n \cdot q$ should be at least 10
• here’s the same distribution ($p=0.13$, and $n$ increasing) plus $N(np, \sqrt{npq})$

Example

• We want to estimate the prob of observing 59 or fewer smokers in a sample of 400
• the true proportion of smokers is $p=0.20$
• Normal approximation: $\mu = np = 80$, and $\sigma = \sqrt{npq} = 8$
  • so use the normal model N(80, 8) to approximate

Compute

• $Z$ score first: $Z = \cfrac{59 - 80}{8} = -2.63$
• The corresponding left tail area is 0.0043.
• the solution with the formula (using the Binomial Distribution) is 0.0041 - approximately equal

Small Intervals

Caution: The normal approximation may fail on small intervals

• Even when the conditions are met, the approximation can still perform poorly
• it’s the case when the range of counts is small

Example:

• want to compute probabilities of observing 69, 70, 71 smokers in 400 people with $p=0.2$
• Binom: 0.0703
• Norm: 0.0476

Reason why:

• 
• (source: Figure 3.19 from OpenIntro)
• normal curve + ares between 69 and 71 is shaded
• outlined area - exact binomial probability
• Normal approx is too fine-grained (area for ND is too slim)
• solution in this case is to add extra areas on both sides (-+0.5 and ) - this is called “Continuity Correction” [http://en.wikipedia.org/wiki/Continuity_correction]

Usage

A Sampling Distribution for a population mean follows this distribution

• see also Binomial Proportion Confidence Intervals
• and Binomial Proportion Test

Sources

• Гмурман В.Е., Теория вероятностей и математическая статистика – 9-е издание. М.: Высш. шк., 2003.
• Data Analysis (coursera)
• OpenIntro Statistics (book)
• http://stat.duke.edu/courses/Spring13/sta101.001/slides/unit2lec4H.pdf
• https://en.wikipedia.org/wiki/Binomial_distribution#Normal_approximation