Question

Does correlation mean the same thing as causation? In other
words, if there exists a strong correlation between two variables,
does that necessarily mean that one variable causes the other?
Support your answer with examples.

Answer #1

Correlation is a statistical measure that describes the
relationship between two or more variables. A positive correlation
indicates that two variables increase or decrease in parallel and a
negative correlation indicates that if one variable increases then
other decreases. A correlation between variables exists then it
does not automatically mean that the change in one variable is the
cause of the change in the values of the other variable.

Causation indicates that one event is the result of the occurrence
of the other event; i.e. there is a causal relationship between the
two events.

So if there exists a strong correlation between two variables
then it does not necessarily mean that one variable causes the
other.

Example: We know, smoking **causes** an increase in
the risk of developing lung cancer. But smoking is
**correlated** with alcoholism, but it **does
not cause** alcoholism.

If two variables are/ have a strong linear correlation, does
this mean that one is the cause of the other? Give your opinion and
support it with examples

Provide a specific reason why finding a correlation between two
variables does not support a hypothesis that one causes the
other?

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...

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...

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....

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...

A) Two variables have a high covariance. This means the two
variables have a strong relationship. T/F
B) For a variable x, the sample mean is 8 and the sample
standard deviation is 2. One of the observations is 15. Is this
observation an outlier?
Group of answer choices
Yes, the z-score is greater than 3
No, the z-score is between -3 and 3
Yes, the z-score is between -3 and 3
No, the z-score is less than -3
C)...

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...

1) In your own words, explain the differences parametric and
nonparametric tests. Also noted which kind is preferred to be
used.
2) Pictured below is an obvious example of correlation vs.
causation. The sun causes ice cream to melt. The sun also causes
people to get sunburns. However, melting ice cream does not cause
sunburns and vice versa; instead, those variables are correlated
with one another. Provide another obvious example of correlation
vs. causation. You may not use an example...

Discussion question please respond By Day 3
Post
a brief explanation of when an observed correlation might represent
a true relationship between variables and why. Be specific and
provide examples.
Below you will see a
statement that was sent responding to response 1 how would you
respond to response two please proive reference like response 1
this is a discussion.
Response 1:
If you collect India's
steel production data and population of 10 consecutive year it will
result in a...

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