Lesson 14: Intro to Probability and Probability Models

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October 17, 2016 at 10:00 am  •  Posted in f16-busad265 by  •  0 Comments

Mon, Oct 17

Review:

Presentation:

  • Intro to Probability
    • Topic worthy of its own course
    • Simulation of outcomes
      • Random phenomenon
        • Individual outcomes are uncertain
        • Regular distribution of outcomes in large number of trials
      • Independent trials
      • Repetition – note coin tosses required to reach 0.5 probability (p. 238)
    • Video
  • Probability Models
    • Sample Space
      • Discrete vs Continuous
      • All possible Outcomes
    • Probability Rules (see p. 246)
      • Rule 1: Any probability is a number between 0 and 1. Or, mathematically, 0 ≤ P(A) ≤ 1
      • Rule 2: All possible outcomes together must have probability = 1.
      • Rule 3: If events A and B have no outcomes in common, they are disjoint and P(A or B) = P(A) + P(B). This is the addition rule.
      • Rule 4: The complement of an event, A = 1 – P(A)
      • Rule 5: If events A and B are independent then P(A and B) = P(A)*P(B). This is the multiplication rule.
    • Examples:
      • Coin Toss – see Example 4.8 on p. 245
        • Flip a coin 4 times. Event A = toss is “Heads” exactly 2 times. What is P(A)?
        • Step 1: Define the Sample Space (all possible outcomes)
        • Step 2: Identify the set of outcomes that satisfy Event A
        • Step 3: P(A) = Number of Event A outcomes ÷ Number of possible outcomes
      • Roll a pair of dice
        • Define Event A (e.g., roll = 7). What is P(A)?
        • Follow Steps 1-3 above
    • Video
    • Exercise 4.21 on p. 255

Activity:

  • Complete Exercises 4.22, 4.24, 4.26 on pp. 255-256

Assignment:

 

 

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