Ch 3: Characterizing and Displaying Multivariate Data Flashcards

1
Q

density function f(y)

A

the relative frequency of occurrence of the random variable y

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2
Q

µ

A

population mean

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3
Q

E (y)

A

the expected value of y

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4
Q

ȳ

A

sample mean

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5
Q

E (ȳ) =

A

µ

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6
Q

var (ȳ) =

A

σ2/n

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7
Q

E (ay) =

A

aE(y) = aµ

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8
Q

Vay (y) =

A

σ2 = E(y - µ)2

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9
Q

S2

A

Sample variacne

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10
Q

E (S2) =

A

σ2

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11
Q

Standard deviation

A

the square root of either the population variance or sample variance

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12
Q

Bivariate Random Variable (x, y)

A

if two variables x and y are measured on each research unit (object or subject)

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13
Q

population covariance

A

Cov (x, y) = σxy = E[ (x-µx) (y-µy) ]

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14
Q

s

A

Sample standard deviation

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15
Q

Orthogonal

A

Variables with zero sample covariance

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16
Q

Correlation

A

Standardized covariance: to find a measure of linear relationship that is invariant to changes of scale, standardized the covariance by dividing by the standard deviations of the two variables

17
Q

Population correlation

A

ρxy

18
Q

Sample correlation

A

rxy

19
Q

ȳ bold

A

the sample mean vector

20
Q

ȳ1 bold

A

the mean of the n observation on the first variable

21
Q

Y bold

A

Data matrix

22
Q

S bold

A

Sample covariance matrix

23
Q

Covariance matrix

A

variance matrix, variance-covariance matrix, dispersion matrix

24
Q

Σ bold

A

Population covariance matrix

25
Q

R bold

A

Sample correlation matrix

26
Q

P bold

A

Population correlation matrix

27
Q

Generalized sample variance | S |

A

A single numerical value for the overall multivariate scale

28
Q

Total sample variance tr(S)

A

the trace of S

29
Q

Imputation

A

filling the missing data