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Practical Statistics Correlation
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There are six statistics that will answer 90% of all questions! 1. Descriptive 2. Chi-square 3. Z-tests 4. t-tests 5. Correlation 6. Regression
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Correlation tests the degree of association between interval and ratio measures.
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Darwin had a problem. How can the associations and degree of differences between observations be expressed in meaningful mathematics?
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Darwin Galton Karl Pearson
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Correlation is based on a very simple idea that Karl Pearson saw….
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If you take two measures of the same person or object, multiple them, and then add the products across persons or objects… such as: Person M1 M2Product 15525 24416 333 9 422 4 511 1 Sum = 55
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The largest possible sum will occur if M1 and M2 are in perfect ordinal order. Note what happens when only one measure changes. Person M1 M2Product 15420 24520 333 9 422 4 511 1 Sum = 54
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The smallest possible sum will occur if M1 and M2 are in perfect inverse order. Person M1 M2Product 151 5 242 8 333 9 424 8 515 5 Sum = 35
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The “normal score” or “standardized score” is equal to:
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Converting the measures to Z scores… Person M1 M2Product 1-1.41-1.41 2.0 2-0.71-0.71 0.5 3 0 0 0 4 0.71 1.71 0.5 5 1.41 1.41 2.0 Sum = 5.0
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Note, that the sum is equal to the number of people in the sample, i.e., 5. Person M1 M2Product 1-1.41-1.41 2.0 2-0.71-0.71 0.5 3 0 0 0 4 0.71 1.71 0.5 5 1.41 1.41 2.0 Sum = 5.0
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Note, what happens when the measure in Z scores are arranging for perfect inverse order: Person M1 M2Product 1-1.41 1.41 -2.0 2-0.71 0.71 -0.5 3 0 0 0 4 0.71 -1.71 -0.5 5 1.41 -1.41 -2.0 Sum = -5.0
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Then X = Y, and :
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This is called “the sum of the cross products”
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So: When X and Y are in ranked order the max will be equal to n, and when ranked in perfect negative order, the max will be -n. (Or, n-1 and –(n-1) if taken from a sample).
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The average of the sum of cross products is the correlation.
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This means that a perfect association always has a value of 1.0 when in positive order and a value of -1.0 when in negative order. A value of zero would indicate a random relationship between the two variables. r = 1.0 or r = -1.0
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How to do this on Excel: http://www.gifted.uconn.edu/siegle/research/Correlation/excel.htm
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The correlation can be graphically shown by using a scatter plot:
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The correlation is related to the shape of the scatter plot: http://en.wikipedia.org/wiki/Scatter_plot http://www.itl.nist.gov/div898/handbook/eda/section3/eda33q.htm
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The correlation is an INDEX of association. It contains three pieces of information:
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The correlation is an INDEX of association. It contains three pieces of information: 1.How much association is present,
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The correlation is an INDEX of association. It contains three pieces of information: 1.How much association is present, 2.Is that a “significant” association, That is, can we reject the H 0 that the true association is zero (there is no association).
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The correlation is an INDEX of association. It contains three pieces of information: 1.How much association is present, 2.Is that a “significant” association, 3.And, what is the magnitude of that association. That is, how much of the change in one variable can be known by looking at the change in the other variable.
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The correlation is an INDEX of association. It contains three pieces of information: The correlation is r….. and r-squared is the amount of variation accounted for in Y by knowing X.
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The correlation is r….. r-squared is the amount of variation accounted for in Y by knowing X.
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Be careful!! The correlation does not tell you that X is the cause of Y. It is a necessary condition for cause, but it does not prove cause…
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That correlation proves causation, is a logical fallacy by which two events that occur together are claimed to have a cause-and-effect relationship. The fallacy is also known as cum hoc ergo propter hoc ( Latin for "with this, therefore because of this ") and false cause.
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The number of people waiting for a bus or train is highly correlated with how long a person must wait for a ride.
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Trains come sooner when more people are waiting for it! Does this mean that It will come sooner if you bring your friends?
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Since the 1950s, both the atmospheric CO 2 level and crime levels have increased sharply.CO 2 Hence, atmospheric CO 2 causes crime.
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Young children who sleep with the light on are much more likely to develop myopia in later life.myopia
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