Agenda Informationer –Uformel evaluering –Status på lektionsplan –Projekt 1 Opsamling fra sidst –Centrumskøn –Variation –Andre begreber Sandsynlighedsregning.

Slides:



Advertisements
Similar presentations
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide
Advertisements

Beginning Probability
Agenda 1.Informationer 2.Opsamling fra sidst a)Spørgeskemaer b)Standardafvigelser 3.Sandsynlighedsregning a)Definitioner b)Regneregler 4.Sandsynlighedsfordeling.
Probability: The Study of Randomness Chapter Randomness Think about flipping a coin n times If n = 2, can have 2 heads (100% heads), 1 heads and.
Agenda Informationer –Uformel evaluering –2 spørgeskemaer + eval. Opsamling fra sidst –Variabel – def. og typer –Fordeling (distribution) –Centrumskøn.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 14 From Randomness to Probability.
Copyright © 2006 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide
1-1 Copyright © 2015, 2010, 2007 Pearson Education, Inc. Chapter 13, Slide 1 Chapter 13 From Randomness to Probability.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 14 From Randomness to Probability.
Section 5.1 and 5.2 Probability
Chapter 4 Probability and Probability Distributions
From Randomness to Probability
Independence and the Multiplication Rule
1 Chapter 6: Probability— The Study of Randomness 6.1The Idea of Probability 6.2Probability Models 6.3General Probability Rules.
Chapter 4: Probability (Cont.) In this handout: Venn diagrams Event relations Laws of probability Conditional probability Independence of events.
Agresti/Franklin Statistics, 1 of 87 Chapter 5 Probability in Our Daily Lives Learn …. About probability – the way we quantify uncertainty How to measure.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 14 From Randomness to Probability.
Mathematics in Today's World
Special Topics. Definitions Random (not haphazard): A phenomenon or trial is said to be random if individual outcomes are uncertain but the long-term.
A multiple-choice test consists of 8 questions
Agresti/Franklin Statistics, 1 of 87 Chapter 5 Probability in Our Daily Lives Learn …. About probability – the way we quantify uncertainty How to measure.
1-1 Copyright © 2015, 2010, 2007 Pearson Education, Inc. Chapter 13, Slide 1 Chapter 13 From Randomness to Probability.
Sample space The set of all possible outcomes of a chance experiment –Roll a dieS={1,2,3,4,5,6} –Pick a cardS={A-K for ♠, ♥, ♣ & ♦} We want to know the.
Probability Distributions. Essential Question: What is a probability distribution and how is it displayed?
From Randomness to Probability
Lecture 03 Prof. Dr. M. Junaid Mughal Mathematical Statistics.
Basic Concepts of Probability Coach Bridges NOTES.
Copyright © 2010 Pearson Education, Inc. Chapter 14 From Randomness to Probability.
Copyright © 2010 Pearson Education, Inc. Chapter 6 Probability.
Copyright © 2010 Pearson Education, Inc. Unit 4 Chapter 14 From Randomness to Probability.
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 14 From Randomness to Probability.
Copyright © 2010 Pearson Education, Inc. Slide
5.1 Probability in our Daily Lives.  Which of these list is a “random” list of results when flipping a fair coin 10 times?  A) T H T H T H T H T H 
5.1 Randomness  The Language of Probability  Thinking about Randomness  The Uses of Probability 1.
From Randomness to Probability Chapter 14. Dealing with Random Phenomena A random phenomenon is a situation in which we know what outcomes could happen,
PROBABILITY IN OUR DAILY LIVES
Sixth lecture Concepts of Probabilities. Random Experiment Can be repeated (theoretically) an infinite number of times Has a well-defined set of possible.
QR 32 Section #6 November 03, 2008 TA: Victoria Liublinska
Section 3.2 Conditional Probability and the Multiplication Rule.
Conditional Probability Mass Function. Introduction P[A|B] is the probability of an event A, giving that we know that some other event B has occurred.
Lecture 7 Dustin Lueker. 2STA 291 Fall 2009 Lecture 7.
Lecture 6 Dustin Lueker.  Standardized measure of variation ◦ Idea  A standard deviation of 10 may indicate great variability or small variability,
+ Chapter 5 Overview 5.1 Introducing Probability 5.2 Combining Events 5.3 Conditional Probability 5.4 Counting Methods 1.
Probability Chapter 3. § 3.1 Basic Concepts of Probability.
1 Chapter 4, Part 1 Basic ideas of Probability Relative Frequency, Classical Probability Compound Events, The Addition Rule Disjoint Events.
Probability theory is the branch of mathematics concerned with analysis of random phenomena. (Encyclopedia Britannica) An experiment: is any action, process.
Chapter 14 From Randomness to Probability. Dealing with Random Phenomena A random phenomenon: if we know what outcomes could happen, but not which particular.
Experiments, Outcomes and Events. Experiment Describes a process that generates a set of data – Tossing of a Coin – Launching of a Missile and observing.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 14 From Randomness to Probability.
Copyright © 2010 Pearson Education, Inc. Chapter 14 From Randomness to Probability.
Chapter 14 Week 5, Monday. Introductory Example Consider a fair coin: Question: If I flip this coin, what is the probability of observing heads? Answer:
AP Statistics Probability Rules. Definitions Probability of an Outcome: A number that represents the likelihood of the occurrence of an outcome. Probability.
Statistics 14 From Randomness to Probability. Probability This unit will define the phrase “statistically significant This chapter will lay the ground.
Chapter 5 Probability in our Daily Lives Section 5.1: How can Probability Quantify Randomness?
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 5 Probability in Our Daily Lives Section 5.1 How Probability Quantifies Randomness.
Probability Models Section 6.2. The Language of Probability What is random? What is random? Empirical means that it is based on observation rather than.
Probability What is the probability of rolling “snake eyes” in one roll? What is the probability of rolling “yahtzee” in one roll?
AP Statistics From Randomness to Probability Chapter 14.
From Randomness to Probability
Dealing with Random Phenomena
Chapter 6 6.1/6.2 Probability Probability is the branch of mathematics that describes the pattern of chance outcomes.
From Randomness to Probability
From Randomness to Probability
From Randomness to Probability
From Randomness to Probability
Honors Statistics From Randomness to Probability
Section 6.2 Probability Models
WARM – UP A two sample t-test analyzing if there was a significant difference between the cholesterol level of men on a NEW medication vs. the traditional.
From Randomness to Probability
From Randomness to Probability
Presentation transcript:

Agenda Informationer –Uformel evaluering –Status på lektionsplan –Projekt 1 Opsamling fra sidst –Centrumskøn –Variation –Andre begreber Sandsynlighedsregning –Definitioner –Regneregler –Uafhængighed Dagens øvelse –Videre med projekt 1

Mean (gennemsnit) The mean is the sum of the observations divided by the number of observations –n betegner antallet af observationer (stikprøvestørrelsen) –y 1, y 2, y 3, … y i,..., y n betegner de n observationer – betegner gennemsnittet It is the center of mass

Standard Deviation (standardafvigelsen) Gives a measure of variation by summarizing the deviations of each observation from the mean and calculating an adjusted average of these deviations. Site Obs SumnGns.Std.afv. A ,0 B ,0 C ,0

A. Learning Objectives 1.Random Phenomena 2.Law of Large Numbers 3.Probability 4.Independent Trials (trail = forsøg / eksperiment) 5.Finding probabilities

Learning Objective 1: Random Phenomena For a random phenomena the outcome is uncertain –In the short-run, the proportion of times that something happens is highly random –In the long-run, the proportion of times that something happens becomes very predictable Probability quantifies long-run randomness

Learning Objective 2: Law of Large Numbers For example, as one tosses a die, in the long run 1/6 of the observations will be a 6. Hvad får vi i det lange løb, hvis vi kaster en terning og berenger andelen, som er større end 3? As the number of trials increase, the proportion of occurrences of any given outcome approaches a particular number “in the long run”

Learning Objective 3: Probability With random phenomena, the probability of a particular outcome is the proportion of times that the outcome would occur in a long run of observations Example: –When rolling a die, the outcome of “6” has probability = 1/6. In other words, the proportion of times that a 6 would occur in a long run of observations is 1/6. Opgave: –Vi tager 1 kort fra en bunke spillekort bestående af i alt 4 x 13 = 52 kort (og lægger det tilbage igen). Hvor stor en andel af gangene får man et rødt kort i første træk (i det lange løb)?

Learning Objective 4: Independent Trials Different trials of a random phenomenon are independent if the outcome of any one trial is not affected by the outcome of any other trial. Example: –If you have 20 flips of a coin in a row that are “heads”, you are not “due” a “tail” - the probability of a tail on your next flip is still 1/2. The trial of flipping a coin is independent of previous flips.

Learning Objective 5: How can we find Probabilities? Observe many trials of the random phenomenon and use the sample proportion of the number of times the outcome occurs as its probability. This is merely an estimate of the actual probability. Calculate theoretical probabilities based on assumptions about the random phenomena. For example, it is often reasonable to assume that outcomes are equally likely such as when flipping a coin, or a rolling a die.

B. Learning Objectives 1.Sample Space (udfaldsrum) for a Trail (forsøg) 2.Event (hændelse) 3.Probabilities for a sample space 4.Probability of an event 5.Basic rules for finding probabilities about a pair of events 6.Probability of the union of two events 7.Probability of the intersection of two events

Learning Objective 1: Sample Space (udfaldsrum) for a Trail (forsøg) The sample space (udfaldsrummet) is the set of all possible outcomes. Udfaldsrummet for en prøve bestående af 3 spørgsmål, som kan besvares korrekt, C (correct), eller forkert, I, (incorrect) fremgår af figuren. Hvad er udfaldsrummet?

Learning Objective 2: Event (hændelse) An event (hændelse) is a subset of the sample space For example; –Event A = a user answers all 3 questions correctly = (CCC) –Event B = a user passes (at least 2 correct) = (CCI, CIC, ICC, CCC) An event corresponds to a particular outcome or a group of possible outcomes.

Learning Objective 3: Probabilities for a sample space Each outcome, f.eks. CCC, in a sample space has a probability The probability of each individual outcome is between 0 and 1. The total (the sum) of all the individual probabilities equals 1.

Learning Objective 4: Probability of an Event The Probability of an event A is denoted by P(A) The Probability is obtained by adding the probabilities of the individual outcomes in the event. When all the possible outcomes are equally likely:

Learning Objective 4: Eksempel: Antal forespørgsler på en hjemmeside 1.Oplist 2 hændelser i ovenstående udfaldsrum. 2.Hvad er ssh. for at en tilfældigt valgt person... a)har kontaktet en hjemmeside med sin mobiltelefon? b)har besøgt en hjemmeside med mere end besøgende? Antal siderMobilPCTotal Under Total

Learning Objective 5: Basic rules for finding probabilities about a pair of events Some events are expressed as the outcomes (udfald) that 1.Are not in some other event (complement of the event) 2.Are in one event and in another event (intersection of two events) 3.Are in one event or in another event (union of two events)

Learning Objective 5: Complement of an event The complement of an event A consists of all outcomes in the sample space that are not in A. The probabilities of A and of A’ add to 1 P(A ’ ) = 1 – P(A)

Learning Objective 5: Disjoint events Two events, A and B, are disjoint if they do not have any common outcomes (udfald)

Learning Objective 5: Intersection of two events (fællesmængde) The intersection of A and B consists of outcomes that are in both A and B.

Learning Objective 5: Union of two events (foreningsmængde) The union of A and B consists of outcomes that are in A or B or in both A and B.

Learning Objective 6: Probability of the Union of Two Events Addition Rule: For the union of two events, P(A or B) = P(A) + P(B) – P(A and B) If the events are disjoint, P(A and B) = 0, so P(A or B) = P(A) + P(B) + 0

Learning Objective 6: Example Event A = Mobil Event B = Site med mere end sider Spm.: Hvordan udregner vi P(A and B) til 0,001? Antal siderMobilPCTotal Under Total

Learning Objective 7: Probability of the Intersection of Two Events Multiplication Rule: For the intersection of two independent events, A and B, P(A and B) = P(A) x P(B) Opgave: Hvad er sandsynligheden for at få to 6’ere ved kast med to terninger? –Definer hændelserne A og B.

Learning Objective 7: Example What is the probability of getting 3 questions correct by guessing (= tilfældigheder)? A=correct. Probability of guessing correctly, P(A)=0,2 What is the probability that a user answers at least 2 questions correctly? P( ) + P( ) + P( ) + P( ) = 0, = 0,104

Learning Objective 7: Events Often Are Not Independent Example: A Music Quiz with 2 Multiple Choice Questions –Data giving the proportions for the actual responses –Events: II IC CI CC –Probability: 0,26 0,11 0,05 0,58 –P(CC) = 0,58

Learning Objective 7: Events Often Are Not Independent Define the events A and B as follows: –A: {first question is answered correctly} –B: {second question is answered correctly} P(A) = P{(CC), (CI)} = = 0.63 P(B) = P{(CC), (IC)} = = 0.69 P(A and B) = P{(CC)} = 0.58 If A and B were independent, P(A and B) = P(A) x P(B) = 0.63 x 0.69 = 0.43 Thus, in this case, A and B are not independent!

Learning Objective 7: Events Often Are Not Independent Don’t assume that events are independent unless you have given this assumption careful thought and it seems plausible. Ø jne ved terningkast M ø ntkast Plat ½ x ⅓ Krone ½ x ⅓

C. Learning Objectives 1.Conditional probability 2.Multiplication rule for finding P(A and B) 3.Independent events defined using conditional probability

Learning Objective 1: Conditional Probability For events A and B, the conditional probability of event A, given that event B has occurred, is: P(A|B) is read as “the probability of event A, given event B.” The vertical slash represents the word “given”. Of the times that B occurs, P(A|B) is the proportion of times that A also occurs

Learning Objective 1: Example 1 What is the probability of a cell phone visit, given the site has ≥ 100,000? –Event A: Cell phone is used –Event B: Site has ≥ 100,000

Learning Objective 1: Regne opgave What is the probability of a cell phone visit given that the site has < pages? A = Cell phone is used B = Pages < P(A and B) = P(B) = P(A|B) = 0,0063 Antal siderMobilPCTotal Under ,00110,17470, ,00090,38190, ,00090,30710, ,00100,13240,1334 Total0,00390,99611,0000

Learning Objective 3: Independent Events Defined Using Conditional Probabilities Two events A and B are independent if the probability that one occurs is not affected by whether or not the other event occurs. Events A and B are independent if: P(A|B) = P(A), or equivalently, P(B|A) = P(B) If events A and B are independent, P(A and B) = P(A) x P(B)

Learning Objective 3: Checking for Independence To determine whether events A and B are independent: –Is P(A|B) = P(A)? –Is P(B|A) = P(B)? –Is P(A and B) = P(A) x P(B)? If any of these is true, the others are also true and the events A and B are independent Ø jne ved terningkast M ø ntkast Plat Krone