Problems - Patrick M Matherne Problem 1 Page 1 Problem 1...

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Patrick M Matherne Problem 1 Page 1 Problem 1 Regression Table k 1 1 2.62280 1 2.62280 4.97379 0.03813 2 2 2.91250 4 5.82500 3.76554 0.00292 3 3 3.13900 9 9.41700 2.93780 0.13433 4 4 4.29520 16 17.18080 0.31114 0.06285 5 5 4.99180 25 24.95900 0.01927 0.16671 6 6 4.64680 36 27.88080 0.04252 0.22629 7 7 5.40080 49 37.80560 0.30008 0.06796 8 8 6.38530 64 51.08240 2.34794 0.03416 9 9 6.74940 81 60.74460 3.59633 0.00010 10 10 7.38640 100 73.86400 6.41812 0.01165 Total 55 48.53000 385 311.38200 24.71253 0.74509 Mean 5.5 4.85300 38.5 31.13820 2.47125 0.07451 Paramater Estimates 1.88853 Xbar 5.50000 Ybar 4.85300 0.53899 38.50000 (XY)bar 31.13820 0.96985 2.47125 0.07451 R 0.98481 X i Y i X i 2 X i* Y i (Y i -Y(bar)) 2 (Y i -b 0 *-b 1 *X) 2 B 0 * B 1 * X 2 bar R 2 S t S r
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Patrick M Matherne Problem 1 Page 2 Regression Line Equation y = 1.88853 + 0.53899x 1 2.43 2 2.97 3 3.51 4 4.04 5 4.58 6 5.12 7 5.66 8 6.2 9 6.74 10 7.28 X i Y i 1 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 Column C Regression Line Eq tion 1 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 Column C Regression Line Eq tion
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Patrick M Matherne Problem 1 Page 3
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Patrick M Matherne Problem 1 Page 4 10 qua- 10 qua-
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Patrick M Matherne Problem 2 Page 5 Problem 2 Linear Fit Regression Table k 1 0.40 750.0 0.160 300.000 1400277.778 95447.43745 2 0.80 1000.0 0.640 800.000 871111.111 2311.98134 3 1.20 1400.0 1.440 1680.000 284444.444 65082.04126 4 1.60 2000.0 2.560 3200.000 4444.444 68717.68110 5 2.00 2700.0 4.000 5400.000 587777.778 28618.26190 6 2.30 3750.0 5.290 8625.000 3300277.778 181100.55222 Total 8.30 11600.0 14.090 20005.000 6448333.333 441277.95527 Mean 1.38 1933.33 2.348 3334.167 1074722.222 73546.32588 Linear Paramater Estimates -165.97 Xbar 1.38333 Ybar 1933.33333 1517.57 2.34833 (XY)bar 3334.16667 0.93 1074722.22 73546.33 R 0.96518 X i M i X i 2 X i* Y i (Y i -Y(bar)) 2 (Y i -b 0 *-b 1 *X) 2 B 0 * B 1 * X 2 bar R 2 S t S r
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Patrick M Matherne Problem 2 Page 6 Linear Fit Equation y = -165.97+ 1517.57X 0.40 441.05 0.80 1048.08 1.20 1655.11 1.60 2262.14 2.00 2869.17 2.30 3324.44 Exponential Fit Equation y = exp(6.25 + 0.84X) 0.40 6.59 727.45 0.80 6.93 1018.66 1.20 7.26 1426.43 1.60 7.60 1997.44 2.00 7.94 2797.02 2.30 8.19 3600.5 X i Y i X i Y i exp(Y i )
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Patrick M Matherne Problem 2 Page 7 Problem 2 Exponential Fit Regression Table k 1 0.40 6.6 0.160 2.648 0.635 0.000931631 2 0.80 6.9 0.640 5.526 0.260 0.000341695 3 1.20 7.2 1.440 8.693 0.030 0.000349783 4 1.60 7.6 2.560 12.161 0.034 0.000001645 5
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Problems - Patrick M Matherne Problem 1 Page 1 Problem 1...

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