Exam 2 - Statistics Flashcards

1
Q

what measures the outcome of a study

A

response variable

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2
Q

what explains or causes changes in the response variables

A

explanatory variable

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3
Q

what kind of plot shows the relationship between two quantitative variables measured on the same individuals

A

scatterplot

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4
Q

when above-average values of one tend to accompany above-average values of the other and below-average values also tend to occur together

A

positive association

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5
Q

when above-average values of one tend to accompany below-average values of the other, and vice versa

A

negative

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6
Q

the direction and strength of the linear relationship between two quantitative variables

A

correlation

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7
Q

the magnitude of r

A

strength

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8
Q

if the correlation is zero, then the slop of the least-squares regression line is

A

zero

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9
Q

b1

A

slope

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10
Q

b0

A

intercept

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11
Q

what is the straight line formula

A

y(hat) = b0+b1x

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12
Q

a straight line where we have data on an explanatory variable and a response variable

A

least-squares regression line

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13
Q

slope’s equation

A

b1=rSy/Sx

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14
Q

intercept equation

A

b0=y-b1x

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15
Q

square of the correlation

A

r^2

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16
Q

the fraction of the variation in the values of y that is explained by the least-squares regression of y on x

A

r^2

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17
Q

the use of a regression line for prediction far outside the range of values of the explanatory variable x used to obtain the line

A

extrapolation

18
Q

the difference between an observed value of the response variable and the value predicted by the regression line

A

residuals

19
Q

if the regression line is a good fit for the data, then

A

no obvious pattern should be shown in the residual plot

20
Q

an observation that lies outside the overall pattern of other observations

A

outliers

21
Q

points that are outliers in the y direction of a scatterplot have…

A

large regression residuals

22
Q

If removing an observation for a statistical calculation markedly changes the result of the calculation then it is

A

influential

23
Q

an association or comparison that holds for all of several groups can reverse direction when the data are combined to form a single group

A

simpson’s paradox

24
Q

when two variables effects on a response variable cannot be distinguished from each other

A

confounding

25
Q

confounded variables can be either

A

explanatory variable or lurking variable

26
Q

the observed association between the variable x and y is explained by a lurking variable z

A

common response

27
Q

the strength of the association influences the …?

A

precision of determining the value of the other variable

28
Q

correlation makes no distinction between

A

explanatory and response variables

29
Q

r has no

A

units

30
Q

correlation requires that both variables be

A

quantitative

31
Q

error =

A

y-b0-b1x

32
Q

so error =

A

y - y(hat)

33
Q

error = noise =

A

distance = residual

34
Q

residual =

A

observed y - predicted y

35
Q

outliers in y direction are

A

residuals (large)

36
Q

outliers in x direction are

A

influential

37
Q

data =

A

signal + noise

38
Q

r^2 =

A

variations explained by model/total variations

39
Q

total variations =

A

variation explained + unexplained

40
Q
A