Final Module (11 & 12) Flashcards

1
Q

Matrix

A

a rectangular array of numbers, symbols, expressions, functions etc. (called entries, or elements), arranged in rows and columns

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2
Q

Vectors

A

matrices with only one row or column

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3
Q

Element-by-element multiplication & division

A

multiplication: 𝐴 ∘ 𝐡
division: 𝐴 ⊘ 𝐡

Basically multiplying and dividing by using the same rules as adding & subtracting instead of the normal matrix rule.

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4
Q

Matrix multiplication is not commutative (true or false)

A

TRUE

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5
Q

Logistic regression

A

a statistical method that models the relationship between a binary response variable and one or more
continuous predictor variables

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6
Q

Examples of Logistic regression

A
  • Predicting whether or not a customer will default on a loan based on their credit score
  • Predicting whether a basketball player will get drafted into the NBA based on average rebounds and points per game
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7
Q

Logistic regression assumes

A

instead of the linear dependence between the response variable and predictor variables 𝑦 = 𝛽0 + 𝛽1π‘₯1 + β‹― + 𝛽𝑛π‘₯𝑛 there is a linear dependence between the β€œlogit” function of probability that the response variable takes the value of 1 and the predictor variables

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8
Q

Parameter risk

A

the risk associated with underestimating or
overestimating the parameters of the model

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9
Q

Process Risk

A

the risk associated with the variability of the
process itself

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10
Q

Model Risk

A

the risk associated with choosing insufficiently
accurate model

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11
Q

Modeling parameter risk using

A

LINEST output

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12
Q

Total Risk =

A

Total Risk = parameter risk + process risk

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13
Q

β€œLogit” function of the probability of response variable to be 1 is modeled as

A

a linear combination of predictors

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14
Q

MLE

A

used to find the set of parameters that provides the best loglikelihood of the model

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