Statistics Correlations
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 Correlations
 Correlation Vs Causation
 Just because two variables are highly correlated does not mean that one has caused the other.
 CORRELATION DOES NOT IMPLY CAUSATION
 Responses
 Common response Both X and Y respond to changes in some unobserved variable. •Ice cream sales and shark attacks both increase during summer.
 Causation
Changes in X cause changes in Y. For example, football weekends cause heavier traffic

•Ice cream sales and the number of shark attacks on
swimmers are correlated.
 CORRELATION DOES NOT IMPLY CAUSATION

•Ice cream sales and the number of shark attacks on
swimmers are correlated.
 Confounding The effect of X on Y is hopelessly mixed up with the effects of other explanatory variables
 Just because two variables are highly correlated does not mean that one has caused the other.
 Pearsons (r)
 This measures the strength of the linear relationship between two variables
 Pearsons r is always between 1 (\) and 1 (/) r=0
 when there seems to be no relationship between x and y to create a linear line, r=0
 explanation of correlations
 •It is called “productmoment” because it is calculated by multiplying the zscores of two variables by one another to get their “product” and then calculating the mean value, which is called a “moment” of these products. –However, the Pearson’s r is rarely computed this way
 When should Pearsons r be used ?
 measures the relationship between any two variables on an interval or ratio scale
 What is a correlation ?
 •Scatterplots are made up of paired X and Y values.
 it expresses quantitatively the magnitude and direction of a relationship
 To describe the relationship with a straight line (linear correlation),

Spearman (rs)

A statistic
that shows the degree of relationship between 2 variables that are arranged in
rank order
 measured on an ordinal scale

A statistic
that shows the degree of relationship between 2 variables that are arranged in
rank order

Interpreting
Coefficient Magnitude
 We have discovered the different ways correlation can be expressed numerically.
 Often 1/1 is described as a strong coreelation with the closer the number is to zero being described as a weaker correlation
 This is not the case as the context must be taken into consideration before this assumption is made.
 Correlation Vs Causation
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