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Binomial Distribution Simplified Revision Notes

Revision notes with simplified explanations to understand Binomial Distribution quickly and effectively.

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17.1.1 Binomial Distribution

What is Binomial Distribution?

This is a probability model that describes a "game" with two outcomes, a "win" or "lose." There is also a fixed number of games you must play.

lightbulbExample

Example: A game of dice is played in which a 66 is considered a "win" and all other outcomes a "lose." This game is played 2020 times. Find the probability that, of these 2020 games, exactly 55 are a "win."

P(Win)=16P(Lose)=56P(\text{Win}) = \frac{1}{6} \quad P(\text{Lose}) = \frac{5}{6}

(205)×(16)5×(56)15=:highlight[0.129](3sf)\binom{20}{5} \times \left(\frac{1}{6}\right)^5 \times \left(\frac{5}{6}\right)^{15} = :highlight[0.129] \quad (3 \text{sf})

(Combinations: 55 winswins, 1515 losseslosses)

A biased coin is tossed with P(H)=0.7P(H) = 0.7 considered a win. If this is done 2525 times, find the probability of exactly 1212 wins.

(2512)×0.712×0.313=:highlight[0.0115](3sf)\binom{25}{12} \times 0.7^{12} \times 0.3^{13} = :highlight[0.0115] \quad (3 \text{sf})

The steps for calculating the probability on a calculator (images of calculator screen are provided) show:

  1. Navigate to Distribution.
  2. Select Binomial PD.
  3. Input:
  • x=12x = 12
  • n=25n = 25
  • p=0.7p = 0.7
  1. Result: P=:highlight[0.01147575294]P = :highlight[0.01147575294]

Notation

The above game with the biased coin can be summarized as follows:

XB(25,0.7)X \sim B(25, 0.7)

This is the distribution of probabilities.

  • The number of wins, XX, has a binomial distribution.
  • We play 2525 times (without fail) / 2525 trials.
  • The probability of a "win" is 0.70.7.
lightbulbExample

Example The probability that a letter gets lost in the post is 0.010.01. On a particular day, a postman has a batch of 20002000 letters. Model this as a binomial distribution, stating the distribution.

XB(2000,0.01)X \sim B(2000, 0.01)

where XX is the number of lost letters (a lost letter is considered a "win").

Find P(X=26)P(X = 26), the probability of exactly 2626 letters being lost:

P(X=26)=(200026)×0.0126×0.991974=:highlight[0.0342]P(X = 26) = \binom{2000}{26} \times 0.01^{26} \times 0.99^{1974} = :highlight[0.0342]

Cumulative Binomial Distribution

This refers to the situations when we are asked to find probabilities of the form P(Xa) P(X \leq a).

lightbulbExample

Example: Let XB(10,0.2)X \sim B(10, 0.2). Find P(X3)P(X \leq 3).

P(X=0)+P(X=1)+P(X=2)+P(X=3)P(X = 0) + P(X = 1) + P(X = 2) + P(X = 3)

P(X3)=0.1074+0.2688+0.3020+0.2013=:highlight[0.879](3sf)P(X \leq 3) = 0.1074 + 0.2688 + 0.3020 + 0.2013 = :highlight[0.879] \quad (3 \text{sf})

A quick way is to use the Binomial CD option on the calculator.

More steps are shown in the calculator screenshots with a final result: P(X3)=:highlight[0.8791261184]P(X \leq 3) = :highlight[0.8791261184]

lightbulbExample

Example: Let XB(15,0.3)X \sim B(15, 0.3). Find P(X7)=:highlight[0.950]P(X \leq 7) = :highlight[0.950].

Find P(X12)=P(X11)=:highlight[0.9999]P(X \leq 12) = P(X \leq 11) = :highlight[0.9999] (4sf4 sf)

(Note: \leq so we must modify the question)

Find P(X>4)P(X > 4) :

Note: For >> or \geq , it helps to list out the outcomes we do want and therefore those we don't want.

Do Want: 4,5,6,4, 5, 6, \dots, 1515

Don't Want: 0,1,2,3,0, 1, 2, 3 , i.e., \leq 33

So,

P(X>4)=1P(X3)P(X > 4) = 1 - P(X \leq 3)

10.2969=:highlight[0.703](3sf)1 - 0.2969 = :highlight[0.703] \quad (3 \text{sf})

Find P(X>10)P(X > 10) :

Do Want: 11,12,11, 12, \dots, 1515

Don't Want: 10,9,10, 9, \dots, 00

1P(X10)=10.9993=:highlight[6.72×104]1 - P(X \leq 10) = 1 - 0.9993 = :highlight[6.72 \times 10^{-4}]

Assumptions Needed for Binomial Model:

  1. The outcome of each trial is independent of all previous trials.
  2. Constant probability of success. Need to rephrase these sentences in context.

Formulae

For XB(n,p):X \sim B(n, p):

P(X=r)=(nr)(pr)(1p)nrP(X = r) = \binom{n}{r} (p^r) (1 - p)^{n - r}

P(Xr) P(X \leq r): Use calculator in Binomial CD mode.

E(X)=npE(X) = np

Var(X)=npqwhereq=1p\text{Var}(X) = npq \quad \text{where} \quad q = 1 - p

lightbulbExample

Example: For XB(12,0.1)X \sim B (12, 0.1), find P(8X<11)P(8 \leq X \lt 11). Do Want: 8,9,108, 9, 10

Diagram illustrating values 00 to 1212, highlighting 8,9,8, 9, and 1010 in red.

P(X10)P(X7)=P(8X<11)P(X \leq 10) - P(X \leq 7) = P(8 \leq X \lt 11)

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