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Unformatted text preview: Meaning of r 2 Worked Example Example x = explanatory variable = temperature (degrees Celsius) y = response variable = yield (kg) x y 18 76.1 19 77.9 20 78.1 21 78.2 22 78.8 23 79.7 24 79.9 25 81.1 26 81.2 27 81.8 28 82.8 29 83.5 1 First check that a regression line makes sense! Plot. Looks great .. 2 Computations x = 23 . 5 y = 79 . 85 s x = 3 . 606 s y = 2 . 302 r = 0 . 9944 Recall = r s y s x = 0 . 635 = y x = 64 . 928 Regression line y = 64 . 928 + 0 . 635 x Predictions x = 32 y = 64 . 928 + 0 . 635(32) = 85 . 25 kg 3 Suppose only had values y . Compute means, standard deviations, histogram. .. Summary statistics mean yield = y = 79 . 85 standard deviation = s y = 2.302 4 Total variation in y data SST 0 = n X i =1 ( y i y ) 2 = 58 . 29 Now include temperature to explain variability in yields. Predicted yields y i = a + bx i replace y ....
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This note was uploaded on 01/03/2012 for the course EE 1244 taught by Professor Drera during the Fall '10 term at Conestoga.
 Fall '10
 drera

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