Additional Topics for Exam 2 Week 6, Wednesday. Is the Binomial Model Appropriate? Situation #1: “How likely is it that in a group of 120 the majority.

Slides:



Advertisements
Similar presentations
Chapter 8: The Binomial Distribution and The Geometric Distribution
Advertisements

Chapter 7 Discrete Distributions. Random Variable - A numerical variable whose value depends on the outcome of a chance experiment.
How likely something is to happen.
In each of the following situations, state whether or not the given assignment of probabilities to individual outcomes is legitimate, that is, satisfies.
Binomial Distributions
Games of probability What are my chances?. Roll a single die (6 faces). –What is the probability of each number showing on top? Activity 1: Simple probability:
16.4 Probability Problems Solved with Combinations.
Binomial & Geometric Random Variables
Discrete probability functions (Chapter 17) There are two useful probability functions that have a lot of applications…
PROBABILITY How is Probability Useful? Making Probability Judgments. How Are Probabilities Determined?
Binomial & Geometric Random Variables §6-3. Goals: Binomial settings and binomial random variables Binomial probabilities Mean and standard deviation.
Probability Models Chapter 17.
BINOMIAL DISTRIBUTION Success & Failures. Learning Goals I can use terminology such as probability distribution, random variable, relative frequency distribution,
Section 2 Probability Rules – Compound Events Compound Event – an event that is expressed in terms of, or as a combination of, other events Events A.
Chapter 7 Lesson 7.5 Random Variables and Probability Distributions
Warm-up Grab a die and roll it 10 times and record how many times you roll a 5. Repeat this 7 times and record results. This time roll the die until you.
4.5 Comparing Discrete Probability Distributions.
Definitions Cumulative – the total from the beginning to some specified ending point. Probability Distribution Function (PDF) – the command on your calculator.
6.2 Homework Questions.
Probability. Sample Spaces and Probability Functions Determining Probabilities Venn Diagrams and Tree Diagrams Conditional Probability Binomial Distributions.
Week 11 - Wednesday.  What did we talk about last time?  Exam 2 post-mortem  Combinations.
Probability The calculated likelihood that a given event will occur
DMR #21 (a) Find the probability that a randomly chosen household has at least two televisions: (b) Find the probability that X is less than 2.
POSC 202A: Lecture 4 Probability. We begin with the basics of probability and then move on to expected value. Understanding probability is important because.
Geometric and Hyper-geometric Distribution. Geometric Random Variable  Take a fair coin and toss it as many times as needed until you observe a head.
At the end of the lesson, students can: Recognize and describe the 4 attributes of a binomial distribution. Use binompdf and binomcdf commands Determine.
Ch. 17 – Probability Models (Day 1 – The Geometric Model) Part IV –Randomness and Probability.
A Collection of Probability Questions. 1. Determine the probability of each of the following situations a) A red card is drawn from a standard deck There.
The Binomial Distributions
Probability Rules.  P and 44  P ,48,51  P ,57,60.
AP Statistics Monday, 30 November 2015 OBJECTIVE TSW begin the study of discrete distributions. EVERYONE needs a calculator. The tests are graded.
Chapter 16 Week 6, Monday. Random Variables “A numeric value that is based on the outcome of a random event” Example 1: Let the random variable X be defined.
This is a discrete distribution. Situations that can be modeled with the binomial distribution must have these 4 properties: Only two possible outcomes.
4.4 Hypergeometric Distribution
1.Addition Rule 2.Multiplication Rule 3.Compliments 4.Conditional Probability 5.Permutation 6.Combinations 7.Expected value 8.Geometric Probabilities 9.Binomial.
Notes and Questions on Chapters 13,14,15. The Sample Space, denoted S, of an experiment is a listing of all possible outcomes.  The sample space of rolling.
Copyright © 2009 Pearson Education, Inc. Chapter 17 Probability Models.
 Ch 17 – Probability Models Objective  We will learn the characteristics of Bernoulli trials and how to calculate probabilities based on geometric models.
Section 12.3 Conditional Probability. Activity #1 Suppose five cards are drawn from a standard deck of playing cards without replacement. What is the.
Probability Intro. Coin toss u Toss two coins 10 times keeping track of the results (head/tails) u Now toss 3 coins 10 times u Make a chart of all the.
Discrete Distributions
Terminologies in Probability
6.3 Binomial and Geometric Random Variables
Section 12.2 Probability.
Math 145 September 25, 2006.
Practice A research was interested in the relation between stress and humor. Below are data from 8 subjects who completed tests of these two traits.
Probability of Simple Events
PROBABILITY.
Chapter 5: Sampling Distributions
Statistics 1: Elementary Statistics
The Binomial and Geometric Distributions
Probability.
Probability.
Terminologies in Probability
Chapter 17 Part 1 The Geometric Model.
Statistics 1: Elementary Statistics
Terminologies in Probability
Terminologies in Probability
Probability of Compound Events
Terminologies in Probability
Probability Notes Please fill in the blanks on your notes to complete them. Please keep all notes throughout the entire week and unit for use on the quizzes.
Probability Probability Principles of EngineeringTM
Probability and Counting
Math 145 June 26, 2007.
Video .
Terminologies in Probability
Math 145 February 12, 2008.
Terminologies in Probability
Probability: What Chance Do You Have?
Figure 8.1 A pair of dice. Figure 8.1. Figure 8.1 A pair of dice. Figure 8.1.
Presentation transcript:

Additional Topics for Exam 2 Week 6, Wednesday

Is the Binomial Model Appropriate? Situation #1: “How likely is it that in a group of 120 the majority may have Type A blood, given that Type A is found in 43% of the population?” Binomial Is Appropriate: n=120, p=43% X = # of people with type A out of 120 Independent

Is the Binomial Model Appropriate? Situation #2: “We roll 50 dice to find the number of times a six appears” Binomial Is Appropriate: n=50, p=1/6 X = # of sixes out of 50 rolls Independent

Is the Binomial Model Appropriate? Situation #3: “We roll 50 dice to find the distribution of the number of spots on the faces” Binomial Is Not Appropriate: For each trial, we’re not recording whether an event happens or not (situation #2 – we roll a six or we don’t). There are more than two possibilities for each trial.

Is the Binomial Model Appropriate? Situation #4: “We deal a card from a deck. Put it back into the deck and shuffle. Deal another card from the deck. Do this 5 times. How likely is it that we picked 5 hearts?” Binomial Is Appropriate: n=5, p=1/4 X = # of hearts out of 5 picks Independent

Is the Binomial Model Appropriate? Situation #5: “We deal 5 cards from a deck and get all hearts. How likely is that?” Binomial Is Not Appropriate: The chances of getting a heart changes as each heart is dealt. P[success]=p has to be the same for every trial in order for it to be Binomial.

Is the Binomial Model Appropriate? Situation #6: “A company realizes that about 10% of its packages are not being sealed properly. In a case of 24 packages, is it likely that more than 3 are unsealed?” Binomial Is Appropriate: n=24, p=10% X = # of packages that are unsealed out of 24 (Only holds if we assume Independents)

Is the Binomial Model Appropriate? Situation #7: “We sample people one at a time until we find five people with blood type A ” Binomial Is Not Appropriate: We don’t have an exact number of trials (n).