Download presentation

Published byHailey Harrington Modified over 4 years ago

1
**Chapter 8: The Binomial Distribution and The Geometric Distribution**

“In God we trust. All others must bring data.” Robert Hayden, Plymouth State College

2
**8.1 The Binomial Distributions (pp. 414 – 432)**

The Binomial distribution is frequently useful in situations where there are two outcomes of interest, such as SUCCESS or FAILURE. It is often used to model real-life situations, and it finds its way into many extremely useful and important statistical applications and computations.

3
The Binomial Setting Each observation is in one of two categories: success or failure There is a fixed number, N, of observations. Observations are independent. Knowing the result of one observation tells you nothing about the other observations. The probability of success is the same for each observation.

4
**If a count, X, has a binomial distribution with N number of observations and p of success, then:**

5
**Example: Die is rolled 60 times. X = number of times a “3” is rolled:**

6
**Another example: small sample size from large population.**

Use of binomial distribution is appropriate. Assumption: 30% of a population is Hispanic Random sample of size 4 is chosen from the population If X is the number of Hispanics in the sample then:

8
Using the TI83+: The probability that the sample contains exactly 2 Hispanics is Binompdf(4, .3, 2) = .2646 The probability that the sample contains 2 or fewer Hispanics is Binomcdf(4, .3, 2) = .9163

9
**It is important to understand when one has a binomial setting and when one does not.**

Consider a shuffled deck of 52 playing cards: Example #1: random card is selected; suit is noted (is it a heart or not); then card is replaced Cards shuffled with random card selected; suit is noted (is it a heart or not); then card is replaced Entire process is repeated 8 more times for a total of 10 random selections X = total number of hearts obtained in 10 trials Binomial setting? Why? X = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10} N =10 p = 0.25 AND each observation is INDEPENDENT of the other

10
**It is important to understand when one has a binomial setting and when one does not.**

Consider a shuffled deck of 52 playing cards: Example #2: random card is selected; suit is noted (is it a heart or not); then card is NOT replaced Cards shuffled with random card selected; suit is noted (is it a heart or not); then card is NOT replaced Entire process is repeated 8 more times for a total of 10 random selections X = total number of hearts obtained in 10 trials Binomial setting? Why NOT?

11
**8.2 The Geometric Distributions pp. 434 – 444**

The AP Syllabus states that you only need to know how to obtain geometric probabilities through simulation. The geometric setting is somewhat similar to that of the binomial. The basic difference is that the geometric setting DOES NOT HAVE A FIXED NUMBER OF OBSERVATIONS.

12
The Geometric Setting Each observation is in one of two categories: success or failure The probability of success is the same for each observation. Observations are independent. Knowing the result of one observation tells you nothing about the other observations. The variable of interest is the number of trials required to obtain the first success.

13
**Example: How many times would you expect to have to roll a single die to get a “6” ?**

Simulate 10 trials using TI83+: randint(1, 6, 10) Trial 1: Trial 2: Trial 3: Trial 4: Trial 5: Trial 6: Trial 7: Trial 8: Trial 9: Trial 10: The mean of rolls for the 10 trials is _________

14
**If p is the probability of success, and q =1 – p is the probability of failure, then:**

p = probability of success on first trial qp = probability of success on second trial q^2(p)=probability of success on the third trial, etc. If X is a variable representing the number of trials until the first success, the expected value of X is

15
Observe that: Also, for 0<q<1, the sum of the infinite series

16
**The probability of rolling a “6” is 1/6**

The probability of rolling a “6” is 1/6. The expected number of rolls before the first success is 1/(1/6) = 6. California Lottery: You choose 6 numbers for {1, 2, 3, …,49, 50, 51} The state randomly selects 6 numbers You win $1 million if your 6 match the 6 selected by the state Your probability of matching all six is So you would expect your first success after playing 18,009,460 times or after 346,336 weeks (6660 years) if you play once a week.

Similar presentations

OK

Chapter 7 Lesson 7.5 Random Variables and Probability Distributions

Chapter 7 Lesson 7.5 Random Variables and Probability Distributions

© 2018 SlidePlayer.com Inc.

All rights reserved.

To ensure the functioning of the site, we use **cookies**. We share information about your activities on the site with our partners and Google partners: social networks and companies engaged in advertising and web analytics. For more information, see the Privacy Policy and Google Privacy & Terms.
Your consent to our cookies if you continue to use this website.

Ads by Google

Download ppt on ceramic disc brake Ppt on network theory concepts Ppt on synthesis and degradation of purines and pyrimidines paired Converter pub to ppt online form Download ppt on endangered species tiger Ppt on dot net Ppt on stock market analysis Ppt on anti rigging voting system Forms of energy for kids ppt on batteries Single room earth view ppt online