Question

One can infer causation from statistical correlation: if variables are highly correlated then one of the variables must be causally related to the other. True or False.

Answer #1

**Basically a causation** is the relationship
between cause and effect. So, when a cause results in an effect,
that's a **causation**. It simply indicates that a
relationship exists, whereas **causation** is more
specific and says that **one** event actually causes
the **other**. correlation does not imply a causation,
it can also be said that a lack of correlation doesn't imply a lack
of causation.

Hence the statement: *One can infer causation from
statistical correlation: if variables are highly correlated then
one of the variables must be causally related to the other is*
**FALSE.**

Correlation =/= Causation
A lot of data is correlated. In some cases, this is because one
variable causes another. In other cases, things are correlated and
neither thing cause the other. I would like for you to think of one
such example. 2 variables must be (believably) correlated, but one
clearly doesn't cause the other. Thus, your response should have 4
parts to it...
~Thing 1
~Thing 2
~Ridiculous claim- thing 1 causes thing 2 or thing 2 causes
thing...

One of the major misconceptions about correlation is that a
relationship between two variables means causation; that is, one
variable causes changes in the other variable. There is a
particular tendency to make this causal error, when the two
variables seem to be related to each other.
What is one instance where you have seen correlation
misinterpreted as causation? Please describe
Can you help me understand how to answer this question

One of the major misconceptions about correlation is that a
relationship between two variables means causation; that is, one
variable causes changes in the other variable. There is a
particular tendency to make this causal error, when the two
variables seem to be related to each other.
What is one instance where you have seen correlation
misinterpreted as causation? Please describe.
an orginal post please

6. True, False, Explain. Adding a variables to a regression that
are highly correlated with the independent variables already
included but not with the dependent variable will increase your
chance of committing type II errors when conducting tests of
statistical significance on the estimated coefficients.

Describe an example in which two variables are strongly
correlated, but changes in one does not cause changes in the other.
Does correlation imply causation?

TRUE OR FALE
1.
Statistical correlation is equivalent to causation.
2.
Causation occurs when a change in a variable causes change in
another.

Can 2 variables be causally related without exhibiting a
correlation? Use citations and references.

1. Describe and example in which two variables are strongly
correlated, but changes in one does not cause changes in the other.
Does correlation imply causation?
2.
A) Give an example of two events that are mutually exclusive
(disjoint).
B) Given an example of two events that are not mutually
exclusive (not disjoint).

Section 2: True or False
Please indicate whether the following statements are True,
False, or Undetermined.
Statement
True
False
Undetermined
Two variables that are highly correlated are causally
related.
Observed points closest to the regression line are given more
weight than points far away (i.e., outliers).
The standard deviation is only useful as a measure of dispersion
for normally distributed variables.
Two variables that have no observed correlation cannot be
causally related.
When a survey sample has a potential bias...

Two variables, A and B, are correlated with Pearsonâ€™s r and the
obtained correlation is -0.23. Both variables are then standardized
to Z scores and the variables, now in Z scores, are correlated
again. Will the correlation between the Z score format of A and B
differ from the original correlation of -0.23? Explain how it may
or may not differ and why?

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