Exam 1 Flashcards

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1
Q

What notation is used to denote an open interval?

A

(a, b) means the interval of all numbers x where a < x < b

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2
Q

What notation is used to denote a closed interval?

A

[a, b] means the interval of all numbers x where a ≤ x ≤ b

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3
Q

What is the definition of a set?

A

A set is a collection of elements.

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4
Q

What symbols are used to denote a finite set?

A

Curly braces { } are used to denote a finite set.

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5
Q

What is the notation for the empty set?

A

The empty set is denoted by either { } or ∅.

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6
Q

True or False: The empty set is a subset of any nonzero set.

A

True

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7
Q

If A = {0, 1}, how many total subsets does A have?

A

Four total subsets.

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8
Q

What is a statistical experiment?

A

A process which generates a single outcome where there is more than one possible outcome, the possible outcomes are known in advance, and the outcome cannot be predicted with 100% certainty.

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9
Q

What is the sample space in a statistical experiment?

A

The set of all possible outcomes of the experiment.

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10
Q

What does an event represent in statistics?

A

An event is any subset of the sample space of a statistical experiment.

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11
Q

What does it mean for two events A and B to be contained in each other?

A

Event A is contained in event B if every outcome in A is also in B.

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12
Q

What is the complement of an event A?

A

The set of all outcomes in the sample space that are NOT in A.

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13
Q

What is the union of two events A and B?

A

The set of all outcomes in either A or B or in both.

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14
Q

What is the intersection of two events A and B?

A

The set of all outcomes in both A and B.

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15
Q

What does it mean for events A and B to be mutually exclusive?

A

Events A and B cannot both occur simultaneously, meaning their intersection is empty.

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16
Q

What are DeMorgan’s Laws?

A

(A ∪ B)’ = A’ ∩ B’ and (A ∩ B)’ = A’ ∪ B’

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17
Q

Fill in the blank: A set is said to be a ______ of a set if all the elements in A are also contained in the set B.

A

subset

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18
Q

What does the notation ∅ represent?

A

The empty set.

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19
Q

What happens to the sample space when the outcomes are infinite?

A

The sample space may contain an infinite number of outcomes.

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20
Q

What are the three ways to define probability?

A
  • Probability as a long-term relative frequency
  • Subjective probability
  • Axiomatic approach to probability
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21
Q

Define probability as a long-term relative frequency.

A

The proportion of times an event occurs in a large number of trials.

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22
Q

What is subjective probability?

A

A type of probability based on personal judgment or experience.

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23
Q

How can personal probabilities change?

A

They can be updated as more information is gathered.

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24
Q

What is Bayesian statistics?

A

A framework for updating probabilities in the presence of new information.

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25
Q

What are the fundamental axioms of probability?

A
  • P(A) ≥ 0
  • P(S) = 1
  • If A and B are mutually exclusive, P(A ∪ B) = P(A) + P(B)
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26
Q

What is the probability of the empty set ∅?

A

P(∅) = 0.

27
Q

In a finite sample space, how is the probability of an event defined?

A

P(A) = # of outcomes in A / # of total outcomes in S.

28
Q

What is the formula for the probability of an intersection of two events?

A

P(A ∩ B) = # of outcomes in A ∩ B / # of outcomes in S.

29
Q

In a finite sample space with equally likely outcomes, how is the probability of an event defined?

A

P(event) = # of outcomes in event / # of total outcomes in sample space.

30
Q

What is the Fundamental Counting Principle?

A

If there are n tasks with k1, k2, …, kn ways to complete each task, then the total number of ways to complete all tasks is k1 × k2 × … × kn.

31
Q

What is a permutation?

A

The arrangement of distinct items in a specific order.

32
Q

What is the formula for finding the number of permutations of k items from a set of n items?

A

P(n, k) = n! / (n - k)!

33
Q

What distinguishes combinations from permutations?

A

Order matters in permutations but not in combinations.

34
Q

What is the formula for combinations?

A

C(n, k) = n! / (k! (n - k)!)

35
Q

Define a partition in the context of combinatorial counting.

A

Ways to place distinct items into groups without regard to order.

36
Q

What is the formula for counting partitions?

A

P = n! / (n1! n2! … nk!)

37
Q

What is the general approach to calculate probabilities when the sample space is finite?

A

P(event) = # of outcomes in event / # of total outcomes in sample space.

38
Q

What is conditional probability

A

the probability of an event occuring given that another event has already occurred.
P(A∣B)= (P(A∩B)) / (P(B))

39
Q

What does it mean for two events to be independent

A

when the occurance of one does not affect the probability of the other
P(A∩B)=P(A)×P(B)
P(A∣B)=P(A)
P(B∣A)=P(B)

40
Q

What is the difference between independence and mutual exclusivity

A

Independence means the occurrence of one event does not affect the probability of the other, while mutual exclusivity means the two events cannot occur together (i.e., P(A∩B)=0
P(A∩B)=0). Independent events can occur together, but mutually exclusive events cannot.

41
Q

What is conditional independence?

A

Two events A and B are conditionally independent given event C if:
P(A∩B∣C)=P(A∣C)×P(B∣C)
P(A∩B∣C)=P(A∣C)×P(B∣C)

Conditional independence does not imply marginal independence.

42
Q

What is Bayes’ Theorem?

A

Bayes’ Theorem relates the conditional and marginal probabilities of two events. For events A and B, it is given by:

P(A∣B)=(P(B∣A)×P(A)) /(P(B))
​where
P(B)=P(B∣A)×P(A)+P(B∣A^c)×P(A^c)

43
Q

What is a random variable?

A

A numeric encoding of an experiment’s outcome, mapping sample space to real numbers

44
Q

What is the support of a random variable

A

the set of all possible values it can take

45
Q

What is the difference between a discrete and continuous random variable

A

Discrete: support is a list of numbers ( finite or countably infinite)
continuous: Support is an interval or union of intervals

46
Q

What is a probability distribution for a discrete random variable?

A

Specifies values and their probabilities, with P(x)∈[0,1] and ∑P(x i)=1.

47
Q

What is a Bernoulli trial?

A

A statistical experiment with two possible outcomes: “success” or “failure.” The probability of success is p.

48
Q

What is the support of a Bernoulli random variable?

A

The support is {0,1}, where:
1 = success,
0 = failure.

49
Q

What is the PMF of a Bernoulli random variable?

A

P(X=x)=p^(x)*(1−p)^(1−x) for x∈{0,1}.

50
Q

What are the expected value and variance of a Bernoulli random variable?

A

E(X)=p,
Var(X)=p(1−p)

51
Q

What is a Binomial random variable?

A

A Binomial random variable X counts the number of successes in n independent Bernoulli trials, each with success probability p. It is denoted as X∼Binomial(n,p)

52
Q

What is the support of a Binomial random variable?

A

The support is {0,1,…,n}, where n is the number of trials.

53
Q

What is a Poisson random variable?

A

A Poisson random variable X counts the number of occurrences of an event in a fixed interval of time or space, where events occur independently at a constant mean rate λ. It is denoted as X∼Poisson(λ).

54
Q

What is the support of a Poisson random variable?

A

The support is {0,1,2,…}, which is countably infinite.

55
Q

What is the scaling property of a Poisson random variable?

A

If X∼Poisson(λ) counts events per unit time/space, then Y∼Poisson(tλ) counts events over t units of time/space.

56
Q

What is a continuous random variable?

A

A random variable whose support (set of possible values) is an interval or union of intervals. It cannot list all possible values, unlike a discrete random variable.

57
Q

What is the probability density function (PDF) of a continuous random variable?

A

A function f(x) that describes the probability distribution of a continuous random variable. It satisfies: f(x)≥0 for all x,
Area under curve =1

58
Q

What is the probability P(a≤X≤b) for a continuous random variable?

A

It is the area under the PDF curve between a and b

59
Q

What is the probability P(X=x) for a continuous random variable?

A

P(X=x)=0 for any specific value
x, because the probability is spread over an interval.

60
Q

What is the cumulative distribution function (CDF) of a continuous random variable?

A

The area under the curve up to that point

61
Q

What is the probability P(X>x) for an exponential random variable X∼Exponential(λ)?

A

P(X>x)=e^(−λx)

62
Q

What is the support of a uniform random variable X∼Unif(a,b)

A

The support is the interval [a,b]

63
Q

What is the support of an exponential random variable X∼Exponential(λ)?

A

The support is [0,∞).