Sign In | Subscribe

Enter your Sign on user name and password.

Forgot password?
  • Follow us on:
Start learning today, and be successful in your academic & professional career. Start Today!
Loading video...
This is a quick preview of the lesson. For full access, please Log In or Sign up.
For more information, please see full course syllabus of AP Statistics
  • Discussion

  • Download Lecture Slides

  • Table of Contents

Bookmark and Share
Lecture Comments (7)

0 answers

Post by Xiang Dong on March 30 at 03:22:42 AM

In example 4, at about16:00, you said that at least one of the bids meant  A or B. But winning both A and B is also called at least one of the bids because winning 2 is at least winning 1.

1 answer

Last reply by: Xiang Dong
Thu Mar 30, 2017 4:32 AM

Post by Renuka Wagh on January 20 at 10:52:54 PM

At 52:44 you end with .25
Isn't it 1- P ( all the same) so 1- .25 = 0.75?
Am i missing something

1 answer

Last reply by: Sandahl Nelson
Thu Jan 12, 2017 5:00 PM

Post by Angeline Pham on January 9 at 10:37:24 PM

Hello, I have a question regarding a homework problem.
"George is concurrently enrolled in both AP Statistics and AP Computer Science I. He has a quiz in both classes today. The probability that he makes an "A" on either his statistics quiz or his computer science quiz is 0.45 or 0.52, respectively. That probability that he makes an "A" on both quizzes today is 0.25. Are the events "making an "A" on the statistics exam" and "making an "A" on the computer science exam" independent? Explain by showing your work"

1 answer

Last reply by: Sandahl Nelson
Thu Mar 17, 2016 4:49 PM

Post by Max Park on March 17, 2016

Hello, I have a question. What is the difference between mutually exclusive and independent? According to the book, they are supposed to be very different but I do not know how they are different.

Probability Overview

Lecture Slides are screen-captured images of important points in the lecture. Students can download and print out these lecture slide images to do practice problems as well as take notes while watching the lecture.

  • Intro 0:00
  • Objectives 0:21
  • Interpreting Probability 0:46
    • Probability of a Random Outcome or the Long Term Relative Frequency
  • Law of Large Numbers 1:42
    • Expected Value
  • Example I: Probability in Poker 2:21
  • Probability Model 4:31
    • Sample Space (S)
    • Event
    • Probabilities
  • Example II: Basketball Free Throws 6:37
    • Part 1: Sample Space
    • Part 2: Event
    • Part 3: Probability
  • Disjoin Events (aka Mutually Exclusive) 11:00
    • Disjoin Events (aka Mutually Exclusive)
  • Example III: Advertising Contracts 12:23
    • Part A: Venn Diagram
  • Probability of Disjoin Events 14:03
    • Probability of Disjoin Events
  • Example IV: Probability of Disjoin Events 15:58
  • Independence vs. Dependence 18:11
    • Independence vs. Dependence
  • Example V: Independence vs. Dependence 20:26
  • Example VI: Independence vs. Dependence 22:23
  • Probability Rules 23:13
    • Probability Rules
  • Probability Notation 23:31
    • P (A or B)
    • P (A and B)
    • P ( A given B happened)
    • P ( not A)
  • Example VII: Probability Notation 25:17
  • Probability Rule Notation 26:49
    • A or B
    • A and B
  • Example VIII: Determine if These Two Events are Independent 29:05
  • Example IX: Conditional Probability of Wining 31:39
  • Example X: Conditional Probability of Students 36:46
    • Part A: Probability
    • Part B: Conditional Probability
    • Part C: Conditional Probability
  • Example XI: Conditional Probability of Children 42:53
    • Part A: All Boys
    • Part B: All Girls
    • Part C: Exactly Two Boys or Exactly Two Girls
    • Part D: At Least One Child of Each Sex
  • Overview 52:52
    • Complement
    • Mutually Exclusive
    • Intersection
    • Union
    • Independent
  • Bayes Rule 56:02
    • Bayes Rule
  • Example XI: Probability & Bayes Rule 59:43
  • Example XII: Probability & Bayes Rule 1:07:49
  • Simulations 1:05:46
    • Simulations
  • Example XIII: Simulations 1:07:10