# AP Statistics Curriculum 2007 Distrib Multinomial

### From Socr

(→Synergies between Binomial and Multinomial processes/probabilities/coefficients) |
m (→Synergies between Binomial and Multinomial processes/probabilities/coefficients) |
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===Synergies between Binomial and Multinomial processes/probabilities/coefficients=== | ===Synergies between Binomial and Multinomial processes/probabilities/coefficients=== | ||

- | * The Binomial vs. Multinomial '''Coefficients''' | + | * The Binomial vs. Multinomial '''Coefficients''' (See this [http://www.ohrt.com/odds/binomial.php Binomial Calculator]) |

: <math>{n\choose i}=\frac{n!}{k!(n-k)!}</math> | : <math>{n\choose i}=\frac{n!}{k!(n-k)!}</math> | ||

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a_1^{i_1} \times a_2^{i_2} \times \cdots \times a_k^{i_k}}</math> | a_1^{i_1} \times a_2^{i_2} \times \cdots \times a_k^{i_k}}</math> | ||

- | * The Binomial vs. Multinomial '''Probabilities''' | + | * The Binomial vs. Multinomial '''Probabilities''' (See this [http://socr.ucla.edu/Applets.dir/Normal_T_Chi2_F_Tables.htm Binomial distribution calculator]) |

: <math>p=P(X=r)={n\choose r}p^r(1-p)^{n-r}, \forall 0\leq r \leq n</math> | : <math>p=P(X=r)={n\choose r}p^r(1-p)^{n-r}, \forall 0\leq r \leq n</math> | ||

: <math>p=P(X_1=r_1 \cap X_1=r_1 \cap \cdots \cap X_k=r_k | r_1+r_2+\cdots+r_k=n)={n\choose i_1,i_2,\cdots, i_k}p_1^{r_1}p_2^{r_2}\cdots p_k^{r_k}, \forall r_1+r_2+\cdots+r_k=n</math> | : <math>p=P(X_1=r_1 \cap X_1=r_1 \cap \cdots \cap X_k=r_k | r_1+r_2+\cdots+r_k=n)={n\choose i_1,i_2,\cdots, i_k}p_1^{r_1}p_2^{r_2}\cdots p_k^{r_k}, \forall r_1+r_2+\cdots+r_k=n</math> |

## Revision as of 06:26, 6 March 2008

## Contents |

## General Advance-Placement (AP) Statistics Curriculum - Multinomial Random Variables and Experiments

The multinomial experiments (and multinomial distribtuions) directly extend the their bi-nomial counterparts.

### Multinomial experiments

A multinomial experiment is an experiment that has the following properties:

- The experiment consists of
**k repeated trials**. - Each trial has a
**discrete**number of possible outcomes. - On any given trial, the probability that a particular outcome will occur is
**constant**. - The trials are
**independent**; that is, the outcome on one trial does not affect the outcome on other trials.

#### Examples of Multinomial experiments

- Suppose we have an urn containing 9 marbles. Two are red, three are green, and four are blue (2+3+4=9). We randomly select 5 marbles from the urn,
*with replacement*. What is the probability (*P(A)*) of the event*A={selecting 2 green marbles and 3 blue marbles}*?

- To solve this problem, we apply the multinomial formula. We know the following:
- The experiment consists of 5 trials, so k = 5.
- The 5 trials produce 0 red, 2 green marbles, and 3 blue marbles; so
*r*_{1}=*r*_{red}= 0,*r*_{2}=*r*_{green}= 2, and*r*_{3}=*r*_{blue}= 3. - For any particular trial, the probability of drawing a red, green, or blue marble is 2/9, 3/9, and 5/9, respectively. Hence,
*p*_{1}=*p*_{red}= 2 / 9,*p*_{2}=*p*_{green}= 1 / 3, and*p*_{3}=*p*_{blue}= 5 / 9.

Plugging these values into the multinomial formula we get the probability of the event of interest to be:

Thus, if we draw 5 marbles with replacement from the urn, the probability of drawing no red , 2 green, and 3 blue marbles is *0.19052*.

### Synergies between Binomial and Multinomial processes/probabilities/coefficients

- The Binomial vs. Multinomial
**Coefficients**(See this Binomial Calculator)

- The Binomial vs. Multinomial
**Formulas**

- The Binomial vs. Multinomial
**Probabilities**(See this Binomial distribution calculator)

### Example

Suppose we study N independent trials with results falling in one of k possible categories labeled 1,2,*c**d**o**t**s*,*k*. Let *p*_{i} be the probability of a trial resulting in the *i*^{th} category, where . Let *N*_{i} be the number of trials resulting in the *i*^{th} category, where .

For instance, suppose we have 9 people arriving at a meeting according to the following information:

- P(by Air) = 0.4, P(by Bus) = 0.2, P(by Automobile) = 0.3, P(by Train) = 0.1

- Compute the following probabilities

- P(3 by Air, 3 by Bus, 1 by Auto, 2 by Train) = ?
- P(2 by air) = ?

### SOCR Multinomial Examples

### References

- SOCR Home page: http://www.socr.ucla.edu

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