Lesson Objective Understand how we can Simulate activities that have an element of chance using probabilities and random numbers Be able to use the random.

Slides:



Advertisements
Similar presentations
BU BU Decision Models Simulation1 Simulation Summer 2013.
Advertisements

Decision Maths Lesson 14 – Simulation. Wiltshire Simulation There are many times in real life where we need to make mathematical predictions. How long.
Lesson Objective Be able to calculate probabilities for Binomial situations Begin to recognise the conditions necessary for a Random variable to have a.
AP STATISTICS Simulation “Statistics means never having to say you're certain.”
Experimental Probability and Simulation
Chapter 4 Mathematical Expectation.
HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2010 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Chapter 7 Probability.
Math notebook, pencil, and possibly calculator. Definitions  An outcome is the result of a single trial of an experiment.  The sample space of an experiment.
Chapter 6: What Do You Expect? Helpful Links:
8-4 Significance of Experimental Results Warm Up Lesson Presentation
Chapter 5 Understanding Randomness
PROBABILITY. Probability Concepts - Probability is used to represent the chance of an event occurring - Probabilities can be represented by fractions,
Sections 4.1 and 4.2 Overview Random Variables. PROBABILITY DISTRIBUTIONS This chapter will deal with the construction of probability distributions by.
Making Inferences for Associations Between Categorical Variables: Chi Square Chapter 12 Reading Assignment pp ; 485.
Simulation Examples Continued
CORE 1 Patterns in Chance. Daily Starter Begin Handout.
What are the chances of that happening?. What is probability? The mathematical expression of the chances that a particular event or outcome will happen.
A multiple-choice test consists of 8 questions
Copyright © 2015, 2011, 2008 Pearson Education, Inc. Chapter 7, Unit A, Slide 1 Probability: Living With The Odds 7.
The smokers’ proportion in H.K. is 40%. How to testify this claim ?
Basic Quantitative Methods in the Social Sciences (AKA Intro Stats) Lecture 4.
Probability refers to uncertainty THE SUN COMING UP FROM THE WEST.
In this chapter we introduce the idea of what it means for something to be truly random. We also investigate techniques for simulating randomness.
1  Event - any collection of results or outcomes from some procedure  Simple event - any outcome or event that cannot be broken down into simpler components.
Sullivan – Fundamentals of Statistics – 2 nd Edition – Chapter 5 Section 1 – Slide 1 of 33 Chapter 5 Section 1 Probability Rules.
Probability Rules!. ● Probability relates short-term results to long-term results ● An example  A short term result – what is the chance of getting a.
Copyright © Cengage Learning. All rights reserved. CHAPTER 9 COUNTING AND PROBABILITY.
Math 15 – Elementary Statistics Sections 7.1 – 7.3 Probability – Who are the Frequentists?
Probability. Probability is the chance that something will occur or happen. Probabilities are written as fractions, decimals, or percents. Probability.
RANDOM SAMPLES. Another way to pick the 50 cars could be the use of a Random Number table.
16.6 Expected Value.
Introduction to Behavioral Statistics Probability, The Binomial Distribution and the Normal Curve.
Probability Simulation The Study of Randomness.  P all  P all.
AP STATISTICS LESSON SIMULATING EXPERIMENTS.
1.3 Simulations and Experimental Probability (Textbook Section 4.1)
Statistical Inference Statistical Inference involves estimating a population parameter (mean) from a sample that is taken from the population. Inference.
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.
Computer Simulation. The Essence of Computer Simulation A stochastic system is a system that evolves over time according to one or more probability distributions.
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.
Essential Questions How do we use simulations and hypothesis testing to compare treatments from a randomized experiment?
Simulating Experiments Introduction to Random Variable.
Probability Distributions, Discrete Random Variables
Simulation Chapter 16 of Quantitative Methods for Business, by Anderson, Sweeney and Williams Read sections 16.1, 16.2, 16.3, 16.4, and Appendix 16.1.
Introduction to Probability – Experimental Probability.
Simulation in Healthcare Ozcan: Chapter 15 ISE 491 Fall 2009 Dr. Burtner.
Simulation. Simulation is a way to model random events, such that simulated outcomes closely match real-world outcomes. By observing simulated outcomes,
Simulations. Simulations – What’s That? Simulations are used to solve probability problems when it is difficult to calculate the answer theoretically.
12.1 Discrete Probability Distributions (Poisson Distribution)
AP STATISTICS LESSON AP STATISTICS LESSON PROBABILITY MODELS.
Inter Arrival Times. Instead of giving a chance that someone, or something arrives in a particular time interval or not, we use the inter arrival times.
Chapter5 Statistical and probabilistic concepts, Implementation to Insurance Subjects of the Unit 1.Counting 2.Probability concepts 3.Random Variables.
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Probability Distributions Chapter 6.
Lesson 10: Using Simulation to Estimate a Probability Simulation is a procedure that will allow you to answer questions about real problems by running.
Probability Imagine tossing two coins and observing whether 0, 1, or 2 heads are obtained. It would be natural to guess that each of these events occurs.
Box models Coin toss = Head = Tail 1 1
Experimental Probability and Simulation
A True/False quiz consists of 8 questions.
5.2 Probability
Binomial Distribution
Chapter 5 Some Important Discrete Probability Distributions
Mean & Variance for the Binomial Distribution
COUNTING AND PROBABILITY
Significance of Experimental Results
8-4 Significance of Experimental Results Warm Up Lesson Presentation
Calculating the mean Example
Calculating the mean Example
Randomness, Probability, and Simulation
Statistics and Probability-Part 5
Simulation Berlin Chen
Presentation transcript:

Lesson Objective Understand how we can Simulate activities that have an element of chance using probabilities and random numbers Be able to use the random number generator on a calculator to simulate a practical situation

The Rnd# button on your calculator generates a random number between 0 and 1 every time you press it. Suppose we wanted to simulate the tossing of a coin: Read the first number after the decimal point if it is 0,1,2,3,4 = Head if it is 5,6,7,8,9 = Tail How do we simulate a fair 6 sided die? How do we simulate a biased 6 sided die where the probabilities are: P(1) = 0.1P(2) = 0.25P(3) = 0.4 P(4) = 0.05 P(5) = 0.1 P(6) = 0.1

Task 1 Use the Rnd# button on your calculator to decide if the Swimming pool can be completed on time using both models.

Task 2 Example 1 A driving instructor keeps records of passes and fails. From his records he finds the following probabilities. (i) Give a rule to use two-digit random numbers to simulate the number of attempts taken to pass the test. (ii) Use your rule to simulate the results for five learner drivers, using the random numbers below. Random numbers: Number of attempts taken to pass the test Probability

Task 3 Example 2 A driving instructor keeps records of passes and fails. From his records he finds the following probabilities. (i) Give rules to use two-digit random numbers to simulate the number of attempts taken to pass the test. (ii) Use your rule to simulate the results for five learner drivers, using the random numbers below. Random numbers: Attempt at driving test Probability of passing test

Simulating Queuing Times There are two basic approaches to model queuing situations: 1)Is to use a random number generator to calculate the times between arrivals – the arrival interval time. 2) Is to split time into chunks and then decide using a random number generator what the probability of someone arriving in that interval actually is. Eg From experiment it has been estimated that 12 people arrive at a petrol station in an hour. 0 – 2 mins probability of someone arriving is 12/30 2 – 4 mins etc

A petrol station wants to install a/some car washes. It always takes 12mins to wash a car in the car wash they intend to purchase. Interval between arrivals (ie. Time since last arrival) Frequency What is the average interval time? How many car washes would you therefore recommend to meet demand?

2 Car Washes 12mins to wash a car Interval between arrivals (ie. Time since last arrival) Frequency Draw up a table to simulate the arrival of cars over a two hour period based on a 2 digit random number.

2 Car Washes 12mins to wash a car Use your table to simulate the queue for the car wash if we assume they install 2 car washes and that there is a single queue with people going to the first available washer.