Unit Four: Probability, Random Variables, and Probability Distributions- essential knowledge

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Do patterns in data necessarily mean that variation is not random?

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42 Terms

1

Do patterns in data necessarily mean that variation is not random?

No, patterns in data do not necessarily mean that variation is not random.

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2

What determines the results generated by a random process?

A random process generates results that are determined by chance.

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3

What is an outcome in the context of a random process?

An outcome is the result of a trial of a random process.

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4

What is an event in probability?

An event is a collection of outcomes.

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5

How is simulation used in probability?

Simulation models random events so that simulated outcomes closely match real-world outcomes.

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6

What is recorded during a simulation to estimate probabilities?

The counts of simulated outcomes and the total count are recorded.

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7

How can the relative frequency of an outcome be used in probability?

The relative frequency can be used to estimate the probability of an outcome or event.

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8

What does the law of large numbers state?

It states that simulated (empirical) probabilities tend to get closer to the true probability as the number of trials increases.

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9

What is the sample space of a random process?

The sample space is the set of all possible non-overlapping outcomes.

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10

How is the probability of an event calculated if all outcomes are equally likely?

Probability = (Number of outcomes in event E) / (Total number of outcomes in the sample space).

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11

What range does the probability of an event fall within?

The probability is between 0 and 1, inclusive.

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12

How do you find the probability of the complement of an event E?

The probability of the complement of E is 1 – P(E).

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13

How can probabilities in repeatable situations be interpreted?

They can be interpreted as the relative frequency with which the event will occur in the long run.

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14

What is the joint probability of events A and B?

The joint probability is the probability of the intersection of A and B, denoted P(A ∩ B).

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15

When are two events considered mutually exclusive or disjoint?

When they cannot occur at the same time, so P(A ∩ B) = 0.

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16

What is conditional probability?

Conditional probability is the probability that event A will occur given that event B has occurred, denoted P(A | B).

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17

What does the multiplication rule state?

The probability that events A and B both occur is P(A ∩ B) = P(A) P(B | A).

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18

When are two events A and B considered independent?

They are independent if knowing whether A has occurred does not change the probability that B will occur.

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19

What is the condition for independence in probability?

If events A and B are independent, then P(A | B) = P(A), P(B | A) = P(B), and P(A ∩ B) = P(A) ⋅ P(B).

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20

How do you calculate the probability that event A or event B (or both) will occur?

Use the addition rule: P(A ∪ B) = P(A) + P(B) – P(A ∩ B).

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21

What are the values of a random variable?

The values are numerical outcomes of random behavior.

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22

What is a discrete random variable?

A variable that can only take a countable number of values, each with a probability, where the sum of all probabilities is 1.

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23

How can a probability distribution be represented?

As a graph, table, or function showing the probabilities associated with values of a random variable.

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24

What does a cumulative probability distribution show?

It shows the probability of being less than or equal to each value of the random variable.

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25

What information does an interpretation of a probability distribution provide?

It provides information about the shape, center, and spread of a population, and allows conclusions about the population of interest.

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26

What is a parameter in the context of a population or distribution?

A parameter is a single, fixed numerical value measuring a characteristic of the population or distribution.

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27

How do you calculate the mean (expected value) for a discrete random variable X?

The mean is calculated as μ = Σ[xiP(xi)].

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28

How do you calculate the standard deviation for a discrete random variable X?

The standard deviation is calculated as σ = √Σ[(xi - μ)^2P(xi)].

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29

How should parameters for a discrete random variable be interpreted?

Using appropriate units and within the context of a specific population.

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30

For random variables X and Y, what is the mean of aX + bY?

The mean is aμx + bμy.

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31

When are two random variables independent?

When knowing information about one does not change the probability distribution of the other.

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32

For independent random variables X and Y, how do you calculate the mean and variance of aX + bY?

The mean is aμx + bμy, and the variance is a^2σx^2 + b^2σy^2.

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33

What happens to the probability distribution when a linear transformation Y = a + bX is applied?

The shape remains the same if a > 0 and b > 0. The mean becomes μy = a + bμx, and the standard deviation becomes σy = bσx.

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34

How can a probability distribution be constructed?

It can be constructed using the rules of probability or estimated with a simulation using random number generators.

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35

What is a binomial random variable?

A variable that counts the number of successes in n independent trials, where each trial has two possible outcomes (success or failure) with a probability of success p.

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36

How do you calculate the probability that a binomial random variable X has exactly x successes?

Use the binomial probability function: P(X = x) = (n choose x)p^x(1 - p)^(n - x).

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37

What are the mean and standard deviation of a binomial random variable?

The mean is μx = np and the standard deviation is σx = √[np(1 - p)].

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38

How should probabilities and parameters for a binomial distribution be interpreted?

Using appropriate units and within the context of a specific population or situation.

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39

What is a geometric random variable?

A variable that gives the number of the trial on which the first success occurs, with each trial having two possible outcomes (success or failure) and a probability of success p.

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40

How do you calculate the probability that the first success occurs on trial x in a geometric distribution?

Use the geometric probability function: P(X = x) = (1 - p)^(x - 1)p.

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41

What are the mean and standard deviation of a geometric random variable?

The mean is μx = 1/p and the standard deviation is σx = √[(1 - p)/p^2].

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42

How should probabilities and parameters for a geometric distribution be interpreted?

Using appropriate units and within the context of a specific population or situation.

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