Calculating a regression equation and other stuff

Calculating a regression equation and other stuff -...

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Calculating a regression equation You need the following information: The number in your sample, N The mean of your dependent variable and The mean of your independent variable, Covariance of xy or S yx = Variance of X or Standard deviation of X Variance of Y or Standard deviation of Y If you want to know the strength and direction of the relationship between your variables X and Y, you would calculate a Pearson’s r, or the correlation coefficient . This value ranges from -1 to +1. The closer to 0 the lower the relationship. The + indicates a positive relationship (as X increases, Y increases). The - indicates a negative relationship (as X increases, Y decreases). If you want to know how much of the variation in your dependent variable Y is explained by variation in your independent variable X then you calculate the
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Unformatted text preview: independent variable X then you calculate the r 2 or the Coefficient of Determination which tells you the % of reduced uncertainty in prediction if you use the regression equation/model that you developed. Note that this is the covariation of xy SQUARED! And that the variations in the denominator are not squared. If you use the standard deviation in the denominators, then you would square them. Also, if you calculate r 2 first and then calculate r by taking the square root, be sure to go back to see if you have a negative covariance and if so then your r should be negative as well. If you use the following table, it will help you calculate all the things you need for all of the above formulas. Calculation table should include: X Y ( ) ( ) 2 ( ) ( ) 2 ( ) ( ) 1 2 3 4...
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