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

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

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
confounded variables can be either
explanatory variable or lurking variable
26
the observed association between the variable x and y is explained by a lurking variable z
common response
27
the strength of the association influences the ...?
precision of determining the value of the other variable
28
correlation makes no distinction between
explanatory and response variables
29
r has no
units
30
correlation requires that both variables be
quantitative
31
error =
y-b0-b1x
32
so error =
y - y(hat)
33
error = noise =
distance = residual
34
residual =
observed y - predicted y
35
outliers in y direction are
residuals (large)
36
outliers in x direction are
influential
37
data =
signal + noise
38
r^2 =
variations explained by model/total variations
39
total variations =
variation explained + unexplained
40