Statistics- Correlations

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  • Created by: Jadepw
  • Created on: 08-03-15 10:43
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  • Correlations
    • Correlation  Vs Causation
      • Just because two variables are highly correlated does not mean that one has caused the other.
      • 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. 
        • Confounding- The effect of X on Y is hopelessly mixed up with the effects of other explanatory variables     
    • 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 “product-moment” because it is calculated by multiplying the z-scores 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    
    • 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.


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