Lesson Objective Be able to calculate probabilities for Binomial situations Begin to recognise the conditions necessary for a Random variable to have a.

Slides:



Advertisements
Similar presentations
Lesson Objective Be able to calculate probabilities for Binomial situations Begin to recognise the conditions necessary for a Random variable to have a.
Advertisements

Presentation on Probability Distribution * Binomial * Chi-square
Chapter 12 Probability © 2008 Pearson Addison-Wesley. All rights reserved.
Chapter 7 Discrete Distributions. Random Variable - A numerical variable whose value depends on the outcome of a chance experiment.
Chapter 6 Some Special Discrete Distributions
Problems Problems 4.17, 4.36, 4.40, (TRY: 4.43). 4. Random Variables A random variable is a way of recording a quantitative variable of a random experiment.
Probability Distributions Discrete. Discrete data Discrete data can only take exact values Examples: The number of cars passing a checkpoint in 30 minutes.
Binomial & Geometric Random Variables
Binomial Distributions
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 5.2.
Binomial & Geometric Random Variables §6-3. Goals: Binomial settings and binomial random variables Binomial probabilities Mean and standard deviation.
Unit 4 Starters. Starter Suppose a fair coin is tossed 4 times. Find the probability that heads comes up exactly two times.
THE BINOMIAL DISTRIBUTION. A Binomial distribution arises in situations where there are only two possible outcomes, success or failure. Rolling a die.
Chapter 11 Data Descriptions and Probability Distributions
Binomial Distributions Calculating the Probability of Success.
P. STATISTICS LESSON 8.2 ( DAY 1 )
Binomial Experiment A binomial experiment (also known as a Bernoulli trial) is a statistical experiment that has the following properties:
Binomial Probability Distribution
Chapter 4. Discrete Random Variables A random variable is a way of recording a quantitative variable of a random experiment. A variable which can take.
Dan Piett STAT West Virginia University Lecture 5.
Lesson Objective Understand what we mean by a Random Variable in maths Understand what is meant by the expectation and variance of a random variable Be.
Unit 11 Binomial Distribution IT Disicipline ITD1111 Discrete Mathematics & Statistics STDTLP 1 Unit 11 Binomial Distribution.
At the end of the lesson, students can: Recognize and describe the 4 attributes of a binomial distribution. Use binompdf and binomcdf commands Determine.
Probability Distributions, Discrete Random Variables
If the probability that James is late home from work on any day is 0.4, what is the probability that he is late home twice in a five-day working week?
Binomial Distribution If you flip a coin 3 times, what is the probability that you will get exactly 1 tails? There is more than one way to do this problem,
Binomial Distributions Chapter 5.3 – Probability Distributions and Predictions Mathematics of Data Management (Nelson) MDM 4U.
Binomial Distributions Chapter 5.3 – Probability Distributions and Predictions Mathematics of Data Management (Nelson) MDM 4U Authors: Gary Greer (with.
6.3 Binomial and Geometric Random Variables
Section 6.3 Day 1 Binomial Distributions. A Gaggle of Girls Let’s use simulation to find the probability that a couple who has three children has all.
1. 2 At the end of the lesson, students will be able to (c)Understand the Binomial distribution B(n,p) (d) find the mean and variance of Binomial distribution.
Starter Toss the two coins and record the results two heads a head and a tail two tails P(two heads) P(a head and a tail) P(two tails)
A federal report finds that lie detector tests given to truthful persons have probability about 0.2 of suggesting that the person is deceptive. A company.
Discrete Distributions
6.3 Binomial and Geometric Random Variables
CHAPTER 6 Random Variables
MATH 2311 Section 3.3.
Today is Tuesday.
Binomial and Geometric Random Variables
Probability 5: Binomial Distribution
8.1 The Binomial Distributions
Determining the theoretical probability of an event
(Single and combined Events)
Discrete Probability Distributions
Samples and Populations
Lesson Objectives At the end of the lesson, students can:
ENGR 201: Statistics for Engineers
Statistics 1: Elementary Statistics
Simple Random Sample A simple random sample (SRS) of size n consists of n elements from the population chosen in such a way that every set of n elements.
CHAPTER 6 Random Variables
Introduction to Probability and Statistics
Chapter 6: Random Variables
Chapter 6: Random Variables
Binomial & Geometric Random Variables
Statistics 1: Elementary Statistics
Chapter 6: Random Variables
Warm Up Imagine a family has three children. 1) What is the probability the family has: 3 girls and 0 boys 2 girls and 1 boy 1 girl and 2 boys 0 girls.
CHAPTER 6 Random Variables
Chapter 6: Random Variables
Chapter 6: Random Variables
Lecture 11: Binomial and Poisson Distributions
CHAPTER 6 Random Variables
Chapter 6: Random Variables
Introduction to Probability and Statistics
Chapter 6: Random Variables
CHAPTER 6 Random Variables
Bernoulli Trials and The Binomial Distribution
MATH 2311 Section 3.3.
Chapter 11 Probability.
Presentation transcript:

Lesson Objective Be able to calculate probabilities for Binomial situations Begin to recognise the conditions necessary for a Random variable to have a Binomial distribution and begin to calculate probabilities using the Binomial formula. I toss a biased coin 3 times. The probability that I flip a head is 1/3 Define the Random Variable X = The number of Heads Draw a probability distribution table for this Random Variable:

Consider the following probability question: I have a fair spinner (as shown) The probability that I get a Red is 1/4 P(Red) = 1/4 X = The number of times I spin a Red in 5 spins Spins: 5 What will the probability distribution table look like?

Consider the following probability question: The probability that I get a Red is …… P(Red) = …… I spin the spinner …. times and count the number of Reds that I get. Spins: .…… What outcomes can I get? What is the probability of each outcome? P(0 Reds) = P(1 Red) = P(2 Reds) = P(3 Reds) = P(4 Reds) = P(5 Reds) = P(6 Reds) = P(7 Reds) = P(8 Reds) =

In general if you repeat an experiment ‘n’ times and the probability of success remains fixed for each. Then you can work out the probability of there being ‘r’ success using the formula: P(‘r’ successes) = pr × (1-p)n-r × nCr Situations that we can use this formula for are called Binomial Situations And the ‘shape’ of the relating probabilities is called a Binomial Distribution. Eg. The probability that Ed gets full marks on his homework is 1/5 Assuming that all homework’s have the same level of difficulty, what is the probability that Ed gets full marks on exactly 3 out of the 8 homeworks?

I roll a fair die 8 times. What is the probability that I get 3 sixes? A biased coin has the probability of getting a head as 1/3. I toss the coin 5 times, what is the probability that I get 3 heads? A factory produces ‘widgets’. The probability that a widget is faulty is 10%. If I check 10 widgets: a) What is the probability that 3 are faulty? b) What is the probability that 0 are faulty? c) What is the probability that less than 4 are faulty?

What are the characteristics of a Random Variable/situation that has a Binomial Distribution?

Lesson Objective Understand the characteristics that a situation must have in order to be modelled using a Binomial distribution Understand the notation connected with a Binomial distribution question and be able to use the Binomial distribution to solve a range of probability problems

What are the characteristics of a Random Variable/situation that has a Binomial Distribution?

What are the characteristics of a Random Variable/situation that has a Binomial Distribution? You have ‘n’ trials. Each independent and each with a two outcomes, success and failure. The probability of success ‘p’ remains fixed for each trial. The Random Variable, X, counts the number of successes in ‘n’ trials. Then we say that X~B(n,p) P(X=r) = nCr pr (1-p)n-r

Which of these situations describes a Binomial distribution: 1) In a hospital the number and sex of babies is recorded as they are born. On a particular day 30 Babies are born. X = The number of boy babies born on that day. 2) I roll a biased die 12 times. X = The number of times I get a score below 5. 3) A bag contains 50 red sweets and 50 blue sweets. I take 12 sweets from the bag. X = The number of red sweets 4) I catch the bus top school every day. IN a month I catch the Bus 25 times. X = The number of times that the bus is late 5) I flip a fair coin 20 times. X = The number of throws until I get my first head. 6) I watch my favourite football team play eight games. X = The number of games that they win. 7) A computer has 5 components. If more than 3 of these components fail the computer will not start. I switch on 50,000 computers. X = The number of computers that fail to start.

Look at the graph of B(10,0.3).

1) Look at each of the Random Variables below 1) Look at each of the Random Variables below. Fill in the gaps in each description: I roll a red die and a blue die 8 times Let X = The number of sixes rolled on the red die We say that X ~ B(…,....) Let Y = Number of evens scored on the blue die We say that Y ~ B(…,....) Let Z = The Number of times the blue die and red die add up to a score over 10 We say that Z ~ B(…,....) a) Find P(X = 2) b) Find P(Y = 3) c) Find P(Z = 2) d) Find P(X ≤ 2) e) Find P(Y < 2) f) Find P(Z ≥ 2) 2) Let X~B(10,0.25) Find a) P(X = 2) b) P(X = 0) c) P(X<4) d) P(X>8)

Lesson Objective Be able to answer exam style questions involving Binomial situations. Begin to use probability tables to calculate cumulative Binomial probabilities Suppose X is a Random Variable X ~ B(8,0.2) a) Describe a situation in real life that could be modelled using this distribution. b) Calculate P(X = 2) c) Calculate P(X ≤ 2) d) Calculate P(X ≤ 8)

The probability that a pen, selected at random from a production line of pens is defective is 0.1. If a sample of 6 pend is taken. Find: a) Probability that the sample contains no defective pens. b) Probability that it contains 5 or 6 defective pens. c) Probability that it contains less than 3 defective pens. Assuming that boys and girls have an equal chance of being born. Find the probability that in a family of 5 children there are more boys than girls. The probability that a shopper chooses Soapysuds when buying washing powder is 0.65. Find the probability that in a sample of 8 shoppers, the number who choose Soapysuds is: a) exactly 3 b) more than 5 1% of a box of a production line of light bulbs are faulty. What is the largest sample size which can be taken if it is required that the probability that there are no faulty bulbs in the sample is greater than 0.5? If X ~ B(n,0.6) and P(X<1) = 0.0256 find n The probability that a target is hit is 0.3. Find the least number of shots which should be fired if the probability that the target is hit at least once is greater than 0.95?

5) Extensive research has shown that 1 person in every 4 is allergic to a particular grass seed. A group of 20 university students volunteer to try out a new treatment. What is the expected number of allergic people in the group? What is the probability that exactly two people in the group are allergic? What is the probability that no more than two people in the group are allergic? How large a sample would be needed for the probability of it containing at least one allergic person to be greater than 99.9%. 6) A circuit board has 5 components. It will fail to work if at least 3 of the components are faulty. If the probability of a faulty component is 3/8. What is the probability that any given circuit board is will not work? If you buy a box of 10 circuit boards. What is the likelihood that more than 1 of the circuit boards in the box is faulty?

The probability that a pen, selected at random from a production line of pens is defective is 0.1. If a sample of 6 pend is taken. Find: a) Probability that the sample contains no defective pens. b) Probability that it contains 5 or 6 defective pens. c) Probability that it contains less than 3 defective pens. Assuming that boys and girls have an equal chance of being born. Find the probability that in a family of 5 children there are more boys than girls. The probability that a shopper chooses Soapysuds when buying washing powder is 0.65. Find the probability that in a sample of 8 shoppers, the number who choose soapy suds is: a) exactly 3 b) more than 5 1% of a box of a production line of light bulbs are faulty. What is the smallest sample size which can be taken if it is required that the probability that there are no faulty bulbs in the sample is greater than 0.5? If X ~ B(n,0.6) and P(X<1) = 0.0256 find n The probability that a target is hit is 0.3. Find the least number of shots which should be fired if the probability that the target is hit at least once is greater than 0.95? 0.5 0.0808 0.428 68 4 9

7) Extensive research has shown that 1 person in every 4 is allergic to a particular grass seed. A group of 20 university students volunteer to try out a new treatment. What is the expected number of allergic people in the group? What is the probability that exactly two people in the group are allergic? What is the probability that no more than two people in the group are allergic? How large a sample would be needed for the probability of it containing at least one allergic person to be greater than 99.9%. 8) A circuit board has 5 components. It will fail to work if at least 3 of the components are faulty. If the probability of a faulty component is 3/8. What is the probability that any given circuit board is will not work? If you buy a box of 10 circuit boards. What is the likelihood that more than 1 of the circuit boards in the box is faulty?

Lesson Objective Discover the formula for the expectation and variance of a Binomial distribution Imagine you have a biased coin with Probability of getting a Head 1/3 Let X = Number of Heads from 3 coin tosses. X ~ B(3, 1/3) Draw up a Probability Distribution table and calculate E(X) and Var(X) exactly Imagine you have another biased coin with Probability of getting a Head ¼ Let Y = Number of Heads from 4 coin tosses. Y ~ B(4, ¼) Draw up a Probability Distribution table and calculate E(Y) and Var(Y) exactly Imagine you have another coin with Probability of getting a Head ½ Let Z = Number of Heads from 5 coin tosses. Z ~ B(5, ½) Draw up a Probability Distribution table and calculate E(Z) and Var(Z) exactly Imagine you have another coin with Probability of getting a Head 2/5 Let W = Number of Heads from 5 coin tosses. W ~ B(5, 2/5) Draw up a Probability Distribution table and calculate E(W) and Var(W) exactly What do you notice about the expectation and variance each time? How does it relate to ‘n’ and ‘p’? Can you prove this?

In general if: You have ‘n’ trials. Each independent and each with a two outcomes, success and failure. The probability of success ‘p’ remains fixed for each trial. The Random Variable, X, counts the number of successes in ‘n’ trials. Then X~B(n,p) P(X=r) = nCr pr (1-p)n-r E(X) = np Var(X) = np(1-p)