Probabilities, Distributions, Expected Value, & IID variables

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Last updated 2:53 AM on 5/13/26
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16 Terms

1
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What does mutually exclusive events mean?

2 events can’t happen at the same time

2
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What is the conditionally probability of B given A

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3
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What does independent events mean, and what’s it notation

  • Probability of second event remains the same even if you know the first event happened

    • A and B are independent if: P(B|A) = P(B)

4
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What does A U B mean

Define new event A or B

  • P(A) or P(B) or both

  • P(A) + P(B) - P(both)

5
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What does A ‘upside down U’ B mean

Define new event A and B

6
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What does Discrete Uniform Distribution mean

  • each possible outcome has the same probability (1/n)

7
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What’s bernouli distribution

probability with 2 outcomes (success & failure) → (1 & 0) → “p” usually means success, and

(1 - p) means failure

8
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What’s a binomial distribution

  •  a sequence of n independent Bernoulli trials

<ul><li><p><span style="background-color: transparent;">&nbsp;a sequence of n independent Bernoulli trials</span></p></li><li><p></p></li></ul><p></p>
9
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What’s a Hypergeometric Distribution

  •  probability of k success in n draws without replacement

10
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What is the equation for a hypergeometric distribution

  • G: tickets marked 1

  • K: number of samples drawn with k tickets marked 1

  • (N - G): tickets marked 0 in the box

  • (N n) – denominator: total simple random samples of size n that can be drawn from pool N

<ul><li><p><span style="background-color: transparent;">G: tickets marked 1</span></p></li><li><p><span style="background-color: transparent;">K: number of samples drawn with k tickets marked 1</span></p></li><li><p><span style="background-color: transparent;">(N - G): tickets marked 0 in the box</span></p></li><li><p><span style="background-color: transparent;">(N n) – denominator: total simple random samples of size n that can be drawn from pool N</span></p></li></ul><p></p>
11
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What are some key properties of Variance

  • Can never be negative

  • Adding constants doesn’t change anything

  • Multiply every outcome by constant a → variance inc by a^2

    • e.g) Var(aX) → a^2 Var(x)

  • Var( X + Y) = Var(X) + Var(Y)

  • Var(X - Y) = Var(X) + Var(Y)

12
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What does expected value tell you?

Tells you where the center of your data is

13
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What does the Central Limit Thm tell you?

Take many samples from a population and calculate each mean —> as samples increase —> distribution normal

14
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Can you give me the coordinates, and probabilities under the Gaussian Distribution

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15
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What is the expected value of the sample mean

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16
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What is the variance of sample mean

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