Correlation Flashcards
a statistical technique that can show whether and how strongly pairs of variables
are related.
Correlation
needed to obtain a measure of relatedness independent of the units of X and Y
correlation coefficient
a dimensionless quantity that is independent of the
units of X and Y and ranges between −1 and 1.
correlation coefficient
For random variables that are approximately
linearly related, a correlation coefficient of 0 implies dependence
False: independence
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.
True
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).
strong
positive correlation
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)
perfect
negative dependence
A somewhat ________________ exists between FEV and number of
cigarettes smoked per day in children (Figure d).
weaker negative correlation
If the correlation is greater than 0, such as for birthweight and estriol, then the variables are
said to be ___________.
positively correlated
Two variables (x, y) are _________ if as x increases, y tends to increase, whereas as x decreases, y tends to decrease.
positively correlated
If the correlation is less than 0, such as for pulse rate and age, then the variables are said to
be ________________.
negatively correlated
Two variables (x, y) are ______________ if as x increases, y tends to decrease, whereas as x decreases, y tends to increase.
negatively correlated
If the correlation is exactly 0, such as for birthweight and birthday, then the variables are said
to be ____________.
uncorrelated
Two variables (x, y) are __________ if there is no linear relationship between x and y.
uncorrelated
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.
True
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
POSITIVE CORRELATION
exists when high scores in one variable are associated
with low scores in the second or vice versa.
NEGATIVE CORRELATION
exists when the points on the scatter diagram are spread in a random manner.
ZERO CORRELATION
all points lie on a straight line
PERFECT CORRELATION
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
Degree/ strength of relationship
Perfect Relationship
very strong/very high
strong/high
moderate/substantial
weak/small
almost negligible to slight
no correlation
𝑟 means
correlation coefficient
n means
sample size
x
value of the independent variable
y
value of the dependent variable
works best with linear relationships: as one variable gets
larger, the other gets larger (or smaller) in direct proportion.
Pearson correlation technique
Pearson correlation technique does not work well with __________ (in which the relationship does not follow a straight line)
curvilinear relationships
example of a curvilinear
relationship
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.
can be used to examine
curvilinear relationships
Multiple regression (also included in the Statistics Module)
