Question

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?

Answer #1

Scatter plot plays a great role in viewing the data in many ways----

1)what is trend of the data, increasing, decreasing or constant.

2) Is any outlier present in the data or not.

why scatter plot is necessary before finding correlation coefficient----

1) it will give pre idea about the relationship between these two variable.

2) it will give idea about the linear relationship between two variable, sometime relation between x and y is non linear but correlation coefficient is not zero(as correlation coefficient is a measure of linear relation between two variable), gives a wrong interpretation of data....By scatter plot we can get an idea about type of relationship linear or nonlinear.

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?
Why is it important to remember “association, not causation”
when discussing correlations? Please provide an example.

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

Correlation is a visual method for determining the relationship
between two variables... including linear, curve linear, strong,
weak, positive, negative, and no relationships. The correlation
coefficient is a mathematical reflection of that relationship.
Regression analysis is the same thing as the correlation
coefficient. True or False

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...

Suppose the correlation coefficient between two variables is
found to be 0.83. Which of the following statements are true?
small values of one variable are associated with large values of
the other variable
the relationship between the variables is weak
a scatter plot of the points would show an upward trend
low values of one variable tend to be paired with low values of
the other variable
there is a strong positive curvilinear relationship between the
variables
there is a...

correlation measures the degree to which two variables are
related to one another.
Here are the definitions of the three possibilities:
Positive correlations: In this type of
correlation, both variables increase or decrease at the same time.
A correlation coefficient close to +1.00 indicates a strong
positive correlation.
Negative correlations: This type of
correlation indicates that as the amount of one variable increases,
the other decreases (and vice versa). A correlation coefficient
close to -1.00 indicates a strong negative correlation....

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...

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

Since a sample data shows that a linear correlation coefficient
between two variables is about 0.08, then it rules out a possible
causal relationship between the two variables.
Yes
No

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