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Correlation and Regression Statistics 2126

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Introduction Means etc are of course useful We might also wonder, “how do variables go together?” IQ is a great example It goes together with so much stuff

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A scatterplot You tend to put the predictor on the x axis and the predicted on the y, though this is not a hard and fast rule A scatterplot is a pretty good EDA tool too eh Pick an appropriate scale for you axes Plot the (x,y) pairs

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So what does it mean If, as one variable increases, the other variable increases we have a positive association If, as one goes up, the other goes down, we have a negative association There could be no association at all

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Linear relationships BTW, I am only talking about straight line relationships Not curvilinear Say like the Yerkes Dotson Law, as far as a the stuff we will talk about, there is no relationship, yet we know there is

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The strength is important too The more the points cluster around a line, the stronger the relationship is Height and weight vs height in cm vs height in inches We need something that ignores the units though, so if I did IQ and your income in real money or IQ and your income in that worthless stuff they use across the river, the numbers would be the same

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The Pearson Product Moment Correlation Coefficient

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Properties of r -1.00 <= r <= +1.00 The sign indicates ONLY the direction (think of it as going uphill or downhill) |r| indicates the strength So, r = -.77 is a stronger correlation than r =.40

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Some examples

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EDA is KEY

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Check these out.. All of these have have the same correlation R =.7 in each case Note the problem of outliers Note the problem of two subpopulations

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Remember this Correlation is not causation I said, correlation is not causation Let me say it again, correlation is not causation Birth control and the toaster method

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Wouldn’t it be nice If we could predict y from x You know, like an equation Remember that in school, you would get an equation, plug in the x and get the y Well surprise surprise, there is a method like this in statistics

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If we are going to predict with a line Well, we will make mistakes We will want to minimize those mistakes

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There is a problem, a common problem Those prediction errors or residuals (e) sum to 0 Damn Though guess what we could do… Why square them of course So we get a line that minimizes squared residuals

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The line will look like this

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In general the equation of the line is….. Y hat (predicted y) Y interceptslope

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This might help

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So…. With a regression line you can predict y from x Just because it says that some value = a linear combination of numbers it does not mean that there is necessarily a causal link Don’t go outside the range Linear only

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