Correlation Flashcards

1
Q

a statistical technique that can show whether and how strongly pairs of variables
are related.

A

Correlation

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2
Q

needed to obtain a measure of relatedness independent of the units of X and Y

A

correlation coefficient

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3
Q

a dimensionless quantity that is independent of the
units of X and Y and ranges between −1 and 1.

A

correlation coefficient

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4
Q

For random variables that are approximately
linearly related, a correlation coefficient of 0 implies dependence

A

False: independence

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5
Q

A correlation coefficient close
to 1 implies nearly perfect positive dependence with large values of X corresponding to large
values of Y and small values of X corresponding to small values of Y.

A

True

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6
Q

example of a ______________ is between forced expiratory volume (FEV), a measure of pulmonary function,
and height (Figure a). A somewhat weaker positive correlation exists between serum cholesterol
and dietary intake of cholesterol (Figure b).

A

strong
positive correlation

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7
Q

A correlation coefficient close to −1 implies ≈ _________, with large values of X corresponding to small values of Y and vice versa,
as is evidenced by the relationship between resting pulse rate and age in children under the age
of 10 (Figure c)

A

perfect
negative dependence

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8
Q

A somewhat ________________ exists between FEV and number of
cigarettes smoked per day in children (Figure d).

A

weaker negative correlation

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9
Q

If the correlation is greater than 0, such as for birthweight and estriol, then the variables are
said to be ___________.

A

positively correlated

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10
Q

Two variables (x, y) are _________ if as x increases, y tends to increase, whereas as x decreases, y tends to decrease.

A

positively correlated

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11
Q

If the correlation is less than 0, such as for pulse rate and age, then the variables are said to
be ________________.

A

negatively correlated

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12
Q

Two variables (x, y) are ______________ if as x increases, y tends to decrease, whereas as x decreases, y tends to increase.

A

negatively correlated

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13
Q

If the correlation is exactly 0, such as for birthweight and birthday, then the variables are said
to be ____________.

A

uncorrelated

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14
Q

Two variables (x, y) are __________ if there is no linear relationship between x and y.

A

uncorrelated

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15
Q

T or F: Thus the sample correlation coefficient provides a quantitative estimate of the dependence
between two variables: the closer |r| is to 1, the more closely related the variables are; if |r| = 1,
then one variable can be predicted exactly from the other.

A

True

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16
Q

Exists when high scores in one variable are associated
with high scores in the second variable or low scores in one variable are associated with
low scores in the other

A

POSITIVE CORRELATION

17
Q

exists when high scores in one variable are associated
with low scores in the second or vice versa.

A

NEGATIVE CORRELATION

18
Q

exists when the points on the scatter diagram are spread in a random manner.

A

ZERO CORRELATION

19
Q

all points lie on a straight line

A

PERFECT CORRELATION

20
Q

Ranges or r (+,-)

1.00
0.90-0.99
0.70-0.89
0.40-0.69
0.20-0.39
0.01-0.19
0

A

Degree/ strength of relationship

Perfect Relationship
very strong/very high
strong/high
moderate/substantial
weak/small
almost negligible to slight
no correlation

21
Q

𝑟 means

A

correlation coefficient

22
Q

n means

A

sample size

23
Q

x

A

value of the independent variable

24
Q

y

A

value of the dependent variable

25
Q

works best with linear relationships: as one variable gets
larger, the other gets larger (or smaller) in direct proportion.

A

Pearson correlation technique

26
Q

Pearson correlation technique does not work well with __________ (in which the relationship does not follow a straight line)

A

curvilinear relationships

27
Q

example of a curvilinear
relationship

A

age and health care.
- They are related, but the relationship doesn’t follow a straight line. Young children and older people both tend to use much more health care than teenagers or
young adults.

28
Q

can be used to examine
curvilinear relationships

A

Multiple regression (also included in the Statistics Module)