chapter 5: discrete random variables Flashcards

1
Q

what is a random variable

A

a variable whose value is numerical and is determined but the outcome of an experiment

it assigns only one numerical value to each experiment outcome

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2
Q

a discrete random variable

A

a random variable who’s possible values can be counted or listed

if you can count them, it can be an infinite number of values

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3
Q

continuous random variable

A

when a random variable can assume any numerical value in one or more intervals on the real number line

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4
Q

how can we model a random variable?

A

by finding its probability distribution

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5
Q

probability distribution, what does it do?

A

it describes how probabilities are distributed over values

of the random variable

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6
Q

what is a discrete probability distribution?

A

probability of a discrete random variable

table, graph, or formula

gives probability associated with each possible value that the random variable can assume

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7
Q

what is p(x)

A

probability distribution of x

x: discrete random variable

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8
Q

what are the two conditions of a discrete probability distribution?

A
  1. p(x) = or > 0

2. the sums of p(x) must be all = to 1

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9
Q

how is the population mean called when dealing discrete random variables

A

the expected value (or mean) of a discrete random variable

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10
Q

how to calculate the expected value (or mean) of a discrete random variable?

A

multiply each value of x by p(x)

sum resulting products over all values of x

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11
Q

which is the most important discrete probability distribution

A

the binomial distribution

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12
Q

what are the characteristics of the binomial distribution (binomial model)

A
  1. experiment consists of n identical trials
  2. each trial results in success or failure
  3. probability of success = p

probability of failure = q = 1 - p

  1. the trials are independent
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13
Q

what is x in the binomial model

A

the total amount of successes in n trials

it is the binomial random variable

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14
Q

what is the rare event approach to a statistical interference?

A

if the probability of an observed sample is small, then we have strong evidence that the assumption is false

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15
Q

whines the probability of an observed sample considered to be small?

A

when it is less than 0.5

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