Expected Value - Revisited
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1 Expected Value - Revisited An experiment is a Bernoulli Trial if: there are two outcomes (success and failure), the probability of success, p, is always the same, the trials are independent.
2 Expected Value - Revisited An experiment is a Bernoulli Trial if: there are two outcomes (success and failure), the probability of success, p, is always the same, the trials are independent. The probability of failure is
3 Expected Value - Revisited An experiment is a Bernoulli Trial if: there are two outcomes (success and failure), the probability of success, p, is always the same, the trials are independent. The probability of failure is 1 p. Suppose we repeat a Bernoulli trial n times.
4 Expected Value - Revisited An experiment is a Bernoulli Trial if: there are two outcomes (success and failure), the probability of success, p, is always the same, the trials are independent. The probability of failure is 1 p. Suppose we repeat a Bernoulli trial n times. How many successes do we expect to get? (what is the expected value, µ?)
5 Expected Value - Revisited An experiment is a Bernoulli Trial if: there are two outcomes (success and failure), the probability of success, p, is always the same, the trials are independent. The probability of failure is 1 p. Suppose we repeat a Bernoulli trial n times. How many successes do we expect to get? (what is the expected value, µ?) How much variance is there (σ 2 ), in the expected number of successes?
6 Flipping a Coin Toss a coin 6 times, and count the number of heads.
7 Flipping a Coin Toss a coin 6 times, and count the number of heads. We are repeating a Bernoulli trial 6 times.
8 Flipping a Coin Toss a coin 6 times, and count the number of heads. We are repeating a Bernoulli trial 6 times. # of heads probability
9 Flipping a Coin Toss a coin 6 times, and count the number of heads. We are repeating a Bernoulli trial 6 times. # of heads probability The expected value is: µ = = 3.
10 Flipping a Coin Toss a coin 6 times, and count the number of heads. We are repeating a Bernoulli trial 6 times. # of heads probability The expected value is: µ = 0 The variance is: σ 2 = (0 3) = (1 3) (6 1) = 3 2.
11 Expected Value and Variance We want better formulas.
12 Expected Value and Variance We want better formulas. In n Bernoulli trials with success probability p, we have:
13 Expected Value and Variance We want better formulas. In n Bernoulli trials with success probability p, we have: µ = np.
14 Expected Value and Variance We want better formulas. In n Bernoulli trials with success probability p, we have: µ = np. σ 2 = np(1 p).
15 The Drake Equation How many civilizations do we expect in the galaxy?
16 The Drake Equation How many civilizations do we expect in the galaxy? We can view this as a Bernoulli trial, by looking at each star.
17 The Drake Equation How many civilizations do we expect in the galaxy? We can view this as a Bernoulli trial, by looking at each star. n is 300 billion.
18 The Drake Equation How many civilizations do we expect in the galaxy? We can view this as a Bernoulli trial, by looking at each star. n is 300 billion. Want p.
19 The Drake Equation The Drake Equation is roughly: p = p planet p life p intelligence p civilization.
20 The Drake Equation The Drake Equation is roughly: p = p planet p life p intelligence p civilization. p planet is the probability that a star has an orbiting planet.
21 The Drake Equation The Drake Equation is roughly: p = p planet p life p intelligence p civilization. p planet is the probability that a star has an orbiting planet. plife is the probability that a planet is capable of sustaining life.
22 The Drake Equation The Drake Equation is roughly: p = p planet p life p intelligence p civilization. p planet is the probability that a star has an orbiting planet. plife is the probability that a planet is capable of sustaining life. pintelligence is the probability that the planet sustains intelligent life.
23 The Drake Equation The Drake Equation is roughly: p = p planet p life p intelligence p civilization. p planet is the probability that a star has an orbiting planet. plife is the probability that a planet is capable of sustaining life. pintelligence is the probability that the planet sustains intelligent life. pcivilization is the probability that an intelligent species develops a civilization.
24 The Drake Equation We know p planet 1.
25 The Drake Equation We know p planet 1. Have to make educated guesses for the other probabilities.
26 The Drake Equation We know p planet 1. Have to make educated guesses for the other probabilities. Estimates are: plife =.13 pintelligence = 1 pcivilization =.2
27 The Drake Equation We know p planet 1. Have to make educated guesses for the other probabilities. Estimates are: plife =.13 pintelligence = 1 pcivilization =.2 So p =.026.
28 The Drake Equation We know p planet 1. Have to make educated guesses for the other probabilities. Estimates are: plife =.13 pintelligence = 1 pcivilization =.2 So p =.026. So µ is approximately 7.8 billion.
29 Complaints? Criticisms:
30 Complaints? Criticisms: Civilizations don t last forever (need more complicated equation).
31 Complaints? Criticisms: Civilizations don t last forever (need more complicated equation). Multiplying probabilities
32 Complaints? Criticisms: Civilizations don t last forever (need more complicated equation). Multiplying probabilities We don t really know plife, p intelligence, and p civilization.
33 Flipping a Coin Going back to flipping a coin 6 times.
34 Flipping a Coin Going back to flipping a coin 6 times. Plot the probabilities of getting k heads,
35 Flipping a Coin Going back to flipping a coin 6 times. Plot the probabilities of getting k heads, and 1 σ 2π (x µ) 2 e 2σ 2
36 Flipping a Coin
37 Flipping a Coin Now flip a coin 20 times.
38 Flipping a Coin Now flip a coin 20 times. What is µ?
39 Flipping a Coin Now flip a coin 20 times. What is µ? What is σ 2?
40 Flipping a Coin Now flip a coin 20 times. What is µ? What is σ 2? Plot the probabilities of getting k heads, and 1 σ 2π (x µ) 2 e 2σ 2
41 Flipping a Coin
42 Flipping a Coin Moral: when n gets large, the distribution of the number of successes looks like a bell-shaped curve.
43 Flipping a Coin Moral: when n gets large, the distribution of the number of successes looks like a bell-shaped curve. Where is the curve centered at?
44 Flipping a Coin Moral: when n gets large, the distribution of the number of successes looks like a bell-shaped curve. Where is the curve centered at? The standard deviation/variance measures how wide the curve is.
45 Flipping a Coin Moral: when n gets large, the distribution of the number of successes looks like a bell-shaped curve. Where is the curve centered at? The standard deviation/variance measures how wide the curve is. The area under the curve is always 1.
46 Normal Distributions If a certain variable is distributed as a bell-shaped curve, we say that the variable follows a normal distribution.
47 Normal Distributions If a certain variable is distributed as a bell-shaped curve, we say that the variable follows a normal distribution. Examples: Bernoulli trials, heights of people, IQ scores, light bulb lifetimes...
48 Normal Distributions If a certain variable is distributed as a bell-shaped curve, we say that the variable follows a normal distribution. Examples: Bernoulli trials, heights of people, IQ scores, light bulb lifetimes... We need to know 2 numbers to describe the normal distribution:
49 Normal Distributions If a certain variable is distributed as a bell-shaped curve, we say that the variable follows a normal distribution. Examples: Bernoulli trials, heights of people, IQ scores, light bulb lifetimes... We need to know 2 numbers to describe the normal distribution: µ: the mean, where the curve is centered. σ: the standard deviation, which specifies how spread out the bell is.
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