Question

Describe the relationship between two variables when the
correlation coefficient *r* is near 0.

Answer #1

The correlation coefficient is a measure that determines the
degree to which two variables' movements are associated. The most
common correlation coefficient, generated by the Pearson
product-moment correlation, may be used to measure the linear
relationship between two variables.

If the correlation coefficient of two variables is zero, it
signifies that there is no linear relationship between the
variables. However, this is only for a linear relationship; it is
possible that the variables have a strong curvilinear
relationship.

.When the value of r is close to zero, generally between -0.1 and +0.1, the variables are said to have no linear relationship or a very weak linear relationship. For example, suppose the prices of coffee and of computers are observed and found to have a correlation of +.0008; this means that there is no correlation, or relationship, between the two variables.

Which statement explains why correlation could be 0 even if a
strong relationship between two variables existed?
Group of answer choices
Since the correlation is 0, there is no strong relationship
between the two variables; and a scatterplot would be
misleading.
Correlation can be 0 even if there is a strong linear
relationship between the variables.
Correlation only measures the strength of the relationship
between two variables when the units of the two variables are the
same.
Correlation does not...

Suppose we have the correlation coefficient for the relationship
between two variables, A and B. Determine whether each of the
following statement is true or false.
(a) The variables A and B are categorical.
(b) The correlation coefficient tells us whether A or B is the
explanatory variable.
(c) If the correlation coefficient is positive, then lower values
of variable A tend to correspond to lower values of variable
B.
(d) If the correlation between A and B is r...

A coefficient of correlation of -0.9 indicates the relationship
between the two variables is
(a) weak and negative
(b) strong and positive
(c) strong and negative

A Correlation Coefficient is a measurement of
the relationship between two variables. A positive correlation
means that as one variable increases, the second variable increases
too. A negative correlation means that as one variable increases,
the second variable decreases, or as one variable decreases, the
second variable increases. Positive and negative correlations
exists in nature, science, business, as well as a variety of other
fields. Please watch the following video for a graphical
explanation of the correlation coefficient:
For Discussion...

How might squaring a correlation coefficient be useful to
understanding the relationship between two variables?

How might squaring a correlation coefficient be useful to
understanding the relationship between two variables?
Why is it important to remember “association, not causation”
when discussing correlations? Please provide an example.

Why is it advisable to generate a scatterplot before computing a
correlation coefficient between two variables? Describe how a
scatterplot might differ when viewing correlations that represent
positive, negative, and no relationship between predictor and
criterion variables. Is it possible to have a relation between
variables that systematic (i.e., reliable and predictable) yet not
linear?

The covariance and correlation coefficient are measures that
quantify the non-linear relationship between two variables.
T/F

Think about a group of people for a moment, then describe a
correlation, (between two variables of your choice) among them. Is
the correlation strong? What would you guess to be the r-value, the
linear correlation coefficient?

When you are presented with a Pearson’s correlation
coefficient between two variables for which an increase in one
predicts a decrease in the other, and vice versa, the Pearson’s
number will be
zero; the Pearson number is only meaningful if the
variables move in the same direction as one another
close to -1 if the correlation is strong, negative but
near zero if the correlation is weak
close to -1 if the correlation is strong, close to +1
if the...

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