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Econometrics > Week 1 > Flashcards

Week 1 Flashcards

1
Q

What is an outcome?

A

It is a mutually exclusive result of an event.

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2
Q

What is probability?

A

It is a way to quantify randomness under the assumption the event can occur infinite amount of times.

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3
Q

What is a random variable?

A

It is a numerical summary of a random outcome.

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4
Q

What are the two classifications of random variables?

A

Discrete and Continuous

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5
Q

What are probability distributions?

A

List of all outcomes and their probabilities associated with each outcome.

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6
Q

What do the probabilities of in a probability distribution add up to?

A

1

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7
Q

Write a list of outcomes and a probability notation.

A
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8
Q

What do all probabilities for continuous random variables add up to? and why can we not list all the outcomes?

A

1 and because they are infinite (continuous data)

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9
Q

How can we summarize probabilities ?

A

We can use probability distribution functions

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10
Q

How to standardise a normally distributed random variable?

A
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11
Q

What are cumulative distribution functions?

A

The probability that a random variable is less than or equal to a value.

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12
Q

How compute a probability of an interval?

A
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13
Q

How to get probabilities for continuous random variables?

A

Integration of the pdf

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14
Q

How to write probability that Z is above 1.96 and what is it?

A
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15
Q

What is the probability that Z is between -1.96 and 1.96?

A
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16
Q

What is the expected value and how is it denoted?

A

It is the long run average value of the random variable over many repeated trials. It is also the weighted average of outcomes. It is denoted as E(Y).

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17
Q

What is variance and how is the formula denoted?

A

It is a measure of spread

18
Q

Something to do with Variance

What is the denotation of standard deviation?

A
19
Q

What is the variance notation for discrete random variables taking on L values?

A
20
Q

What is joint probability distribution along with its notation?

A
21
Q

What is marginal probability distribution with its notation?

A
22
Q

What is conditional probability distribution along with its notation and formula?

A
23
Q
A
24
Q

What is conditional expectation of Y given X and its notation?

A

E(Y ‘subscript: i’ |X ‘subscript: i’ ) is a function where you plug in x ‘subscript: ij’ and you get an average value of Y ‘subscript i’

25
Q

How to get conditional expectation at a particular x ‘subscript ij’ from the conditional distribution?

A
26
Q

What is the law of iterated expectation with its denotation?

A
27
Q

What is independence?

A

Two variables are independent if knowing one does not give information about the other.

28
Q

How to show independence for discrete random variables?

A
29
Q

How to show independence for conditional distribution and its notation?

A
30
Q

How to show mean independence?

A
31
Q

What is covariance and what is it’s notation along with formula?

A
32
Q

What is correlation and its notation along with formula?

A
33
Q

What are the 3 properties of expectations?

  • Adding a constant
  • Sum of random variables
  • Constant times a random variable
A
34
Q

What are the 3 properties of variance?

  • Adding a constant
  • Multiplying a constant
  • Sum of random variables
A
35
Q

What are the 4 properties of covariance?

  • Covariance with a constant
  • Covariance between a random variable and itself
  • Sum of random variables
  • Independence
A
36
Q

In a simple random sample, what do the n observations being i.i.d. stand for?

A

Independent, Identical and Distributed. Knowing Y ‘subscript i’ gives no information about Y ‘subscript j’ . Both come from the same population, coming from the same distribution.

37
Q

How is sample average denoted and its formula?

A
38
Q

What is the expectation of a sample mean when data are identically distributed?

A
39
Q

What is the variance of the sample mean when data is i.i.d. ?

A
40
Q

What is the Law of Large Numbers

A

If we have a large enough sample, sample mean is good approximation for population mean

41
Q

What is the Central Limit Theorem

A

If we have a large enough sample, sampling distribution of the sample mean is approximate, so we can compute probabilities regarding the sample mean.

Econometrics (4 decks)

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