Discrete Probability Flashcards

1
Q

Discrete probability

A

A discrete probability can only take certain values.

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2
Q

Discrete random variable

A

represents a possible numerical value from an
uncertain event. Can only assume a countable number of values. Examples: Roll
a dice twice. Let X be the number of times 4 comes up, thus X could be 0, 1, or 2
times. Toss a coin 5 times. Let X be the number of heads, thus X could = 0, 1, 2,
3, 4, or 5.

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3
Q

Covariance

A

The covariance measures the direction of a linear relationship between two variables. (Association)

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4
Q

4 essential properties of the binomial distribution

A

A fixed number of observations

Two mutually exclusive and collectively exhaustive events

Constant probability for each observation

Observations are independent

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5
Q

Fixed number of observations

A

A fixed number of observations, or trials, n.

E.g. 15 tosses of a coin; ten light bulbs taken from a warehouse.

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6
Q

Two mutually exclusive and collectively exhaustive events

A

Two mutually exclusive and collectively exhaustive categories. E.g. head or tail in each toss of a coin; defective or not defective light bulb. Generally called ‘success’ and ‘failure’. Probability of success is p, probability of failure is 1–p.

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7
Q

Constant probability for each observation

A

Constant probability for each observation. E.g. Probability of getting a tail is the same each time we toss the coin.

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8
Q

Observations are independent

A

Observations are independent. The outcome of one observation does not affect the outcome of the other. Two sampling methods can be used to ensure independence; either: Selected from infinite population without replacement or selected from finite population with replacement.

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9
Q

4 possible binomial scenarios

A

A manufacturing plant labels items as either defective or acceptable.

A firm bidding for contracts will either get a contract or not.

A market research firm receives survey responses of ‘yes I will buy’ or ‘no I will not’.

New job applicants either accept the offer or reject it.

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10
Q

Index numbers

A

Index numbers allow relative comparisons over time. Index
numbers are reported relative to a Base Period Index. Base period index = 100 by
definition. Used for an individual item or measurement.

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11
Q

Aggregate price index

A

An aggregate index is used to measure the rate of change from a base period for a group of items.

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12
Q

Which price index to use

A

Paasche is more accurate but more difficult to achieve.

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13
Q

2 types of random variables

A

Photo 1

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14
Q

Discrete probability distribution toss 2 coins example

A

Photo 2

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15
Q

Expected value

A

Photo 3

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16
Q

Variance and standard deviation of discrete random variables

A

Photos 4-5

17
Q

Covariance formulas

18
Q

Computing the mean example

19
Q

Computing the standard example

20
Q

Computing the covariance example

21
Q

Interpreting mean, standard deviation and covariance

22
Q

The sum of two random variables

23
Q

Portfolio expected return and expected risk

24
Q

Portfolio example

25
Probability distributions
Photo 14
26
Rule of combinations
Photo 15
27
Binomial distribution formula
Photo 16
28
Binomial probability example
Photo 17
29
Shape of the binomial distribution
Photo 18
30
Mean and variance of the binomial distribution
Photo 19
31
Simple price index
Photo 20
32
Index numbers example
Photo 21
33
Index numbers interpretation
Photo 22
34
Aggregate price indexes
Photo 23
35
Unweighted aggregate price index
Photo 24
36
Unweighted aggregate price index example
Photo 25
37
Weighted aggregate price indexes
Photo 26