Stats3 Day 1 Chapter 11- using random # table. Do Now Read Handout 1 2 3 4.

Slides:



Advertisements
Similar presentations
Copyright © 2010 Pearson Education, Inc. Slide A small town employs 34 salaried, nonunion employees. Each employee receives an annual salary increase.
Advertisements

Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 11 Understanding Randomness.
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.”
D1: 5.1 The Study of Randomness h.w: p 293: 1–11 odd, 15,17
Chapter 5 Understanding Randomness
PROBABILITY. Probability Concepts - Probability is used to represent the chance of an event occurring - Probabilities can be represented by fractions,
Lesson  In this investigation you will explore the predictability of random outcomes. You will use a familiar random process, the flip of a coin.
3.6: Probabilities Through Simulations Objective: To simulate probabilities using random number tables and random number generators CHS Statistics.
Understanding Randomness
Probability.
Chapter XI Rory Nimmons Venkat Reddy UnderstandingRandomnessUnderstandingRandomness.
Understanding Randomness
CORE 1 Patterns in Chance. Daily Starter Begin Handout.
Chapter 11: understanding randomness (Simulations)
+ AP Statistics: Chapter 11 Pages Rohan Parikh Azhar Kassam Period 2.
Copyright © 2010 Pearson Education, Inc. Unit 3: Gathering Data Chapter 11 Understanding Randomness.
Chapter 11 Randomness. Randomness Random outcomes –Tossing coins –Rolling dice –Spinning spinners They must be fair.
From Randomness to Probability
Section 6.3 Binomial Distributions. A Gaggle of Girls Let’s use simulation to find the probability that a couple who has three children has all girls.
Slide 11-1 Copyright © 2004 Pearson Education, Inc.
1-1 Copyright © 2015, 2010, 2007 Pearson Education, Inc. Chapter 10, Slide 1 Chapter 10 Understanding Randomness.
Understanding Randomness Chapter 11. Why Be Random? What is it about chance outcomes being random that makes random selection seem fair? Two things: –
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 11 Understanding Randomness.
P. STATISTICS LESSON 8.2 ( DAY 1 )
Understanding Randomness
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 11 Understanding Randomness.
AP STATISTICS Objective: Understanding Randomness Do Now: Take out any completed contracts, personal profiles, as well as your written design study. HW:
Chapter 11 Understanding Randomness. What is Randomness? Some things that are random: Rolling dice Shuffling cards Lotteries Bingo Flipping a coin.
Slide Understanding Randomness.  What is it about chance outcomes being random that makes random selection seem fair? Two things:  Nobody can.
Journal: 1)Suppose you guessed on a multiple choice question (4 answers). What was the chance that you marked the correct answer? Explain. 2)What is the.
Copyright © 2006 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide
Warm Up If Babe Ruth has a 57% chance of hitting a home run every time he is at bat, run a simulation to find out his chances of hitting a homerun at least.
+ The Practice of Statistics, 4 th edition – For AP* STARNES, YATES, MOORE Chapter 5: Probability: What are the Chances? Section 5.1 Randomness, Probability,
Ch. 17 – Probability Models (Day 1 – The Geometric Model) Part IV –Randomness and Probability.
Simulating Experiments Introduction to Random Variable.
Randomness, Probability, and Simulation
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.
Unit 5: Probability: What are the Chances?
Simulation. Simulation is a way to model random events, such that simulated outcomes closely match real-world outcomes. By observing simulated outcomes,
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide
Chapter 10 Understanding Randomness. Why Be Random? What is it about chance outcomes being random that makes random selection seem fair? Two things: –
1 Chapter 11 Understanding Randomness. 2 Why Be Random? What is it about chance outcomes being random that makes random selection seem fair? Two things:
Binomial Distributions Chapter 5.3 – Probability Distributions and Predictions Mathematics of Data Management (Nelson) MDM 4U Authors: Gary Greer (with.
Unit 5 Lesson 6 Probability 6.7 Estimating Probabilities using Simulations.
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.
Copyright © 2009 Pearson Education, Inc. Chapter 11 Understanding Randomness.
Bell Work1/29 1) From the sequence of random numbers, select 3 distinct numbers (no repeats) between 1 and 50, reading from left to right
Statistics 11 Understanding Randomness. Example If you had a coin from someone, that they said ended up heads more often than tails, how would you test.
“Not the real deal but close” Ch 11 Simulations. Real World Example This is a simulation of what it feels.
1 Copyright © 2014, 2012, 2009 Pearson Education, Inc. Chapter 9 Understanding Randomness.
Chapter 8: The Binomial and Geometric Distributions 8.2 – The Geometric Distributions.
Chapter 11 Understanding Randomness. What is the most important aspect of randomness? It must be fair. How is this possible? 1) Nobody can guess the outcome.
Experiments vs. Observational Studies vs. Surveys and Simulations
Negative Binomial Experiment
Chapter Randomness, Probability, and Simulation
Understanding Randomness
Understanding Randomness
Understanding Randomness
Understanding Randomness
Understanding Randomness
CHAPTER 5 Probability: What Are the Chances?
CHAPTER 5 Probability: What Are the Chances?
Understanding Randomness
Binomial Distributions
Experiment Design and Simulation
Understanding Randomness
Presentation transcript:

Stats3 Day 1 Chapter 11- using random # table

Do Now Read Handout

Randomness Statistics: See past the randomness and find patterns and relationships in observations Rely on randomness to avoid biases

Modeling Continue with modeling, but now… Simulation Models

Coin Flipping If someone handed you a coin and told you it’s biased to landing on heads, what would you do? Experiments! How many times are enough? Would 3 heads in a row convince you? Why not? Imagine we did 100 trials, would 54/100 heads convince you? 60? What number would convince you? When determining simulation models, we need to think about number of experiments When determining simulation models, we need to think about considering what is “usual” What about 95/100?

Random # Table What if we wanted to model the situation but didn’t have coins? We only have dice… What if we didn’t have either? Or what if we needed more than 6 outcomes? We know there are only two outcomes (heads or tails), so we determine half(1/2) the numbers to be 1 outcome (heads) and half the numbers to be the other outcome (tails). Random Number Table!! Designate numbers (0-9, 00-99, etc,) to be a certain outcome.

Using Random #s Let’s generate some trials for coin flipping using the random number table! 1. What are our possible outcomes? 2. What numbers should we designate for our outcomes? 3. Determine simulation response. 4. How many trials? 5. Analyze by taking average.

Another Example Derrick Rose has an 80% free throw success rate. How can we use random numbers so simulate whether or not he makes a foul shot? How many shots might he be able to make in a row without missing? 1. Determine which numbers (0-9) represent success (a good shot) and fail (a miss) 2. Simulate shooting until you reach a “miss” for a number of trials 3. Mean number of shots made

How does the simulation change if his free throw percentage was 72% How would it change if we wanted to know how many shots he might make out of 5 chances? How would it change if we want to know his chances of making both shots? What if it was a 1-and-1 situation?

Steps for Simulations 1. Identify the component to be repeated 2. Explain how you will model the outcome 3. Explain how you will simulate the trial 4. State response variables 5. Run several trials 6. Analyze 7. State your conclusion There is a competition for $250 grocery gift card at Jewel. They are placing 1 card of 3 variations in cereal boxes. 20% have card A, 30% card B, and 50% card C. If you get all 3 you could win $250 gift card. We want to use a simulation to give us insight into how many boxes of cereal we need to open until we get all 3 cards. Cereal box selection 0,1 = card A 2,3,4= card B 5,6,7,8,9=card C # of boxes to get “success” Average response

Another Simulation Suppose we randomly select 3 students from class to speak at RCPU about how awesome Stats is. How likely is it that it will be all boys? (consider the gender ratio of our class)

Exit Ticket

Homework Chapter 11 #9, 11, 12, 13