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m=43−103−0=333=11
y−y1=m(x−x1)y−10=11(x−0)y−10=11xy=11x+10
Correlation
A measure of the strength of linear association between two variables.
Always between −1.0 and +1.0.
Can be positive or negative.
Causation
Is a demonstrable cause and effect.
Can be measured by controlled studies.
Should not be assumed even when correlation is strong and predictable.
Correlation and causation are two relationships between data sets.
In causation, one set of data directly causes the other to occur.
In correlation, the two sets have a measurable linear relationship known as the correlation coefficient.
Correlation can be demonstrated by graphing a linear function, by expression as a mathematical model (y=mx + b), or by demonstration in a table of values.
The table below summarizes the differences between correlation and causation.
Relationship
Correlation
A measure of the strength of linear association between two variables
Always between −1.0 and +1.0
Can be positive or negative
Causation
Is a demonstrable cause and effect
Can be measured by controlled studies
Should not be assumed even when correlation is strong and predictable
The correlation coefficient (r) is usually determined with technology. It is a number that describes how closely the numbers in the data set are related. If the r value is positive, there is a positive relationship. If it is negative, the relationship is negative. If the two variables are not correlated at all, the correlation coefficient will be 0.
Strong and weak correlations are a little more subjective in that there is no exact cutoff between strong and weak, but generally, any r value that is close to either 1 or negative 1 is considered strong. Any r value that is closer to 0 is considered weak.
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