Regression_second_order - 0.99 Standard Error 0.03...

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Example to be used in class to illustrate a second-order polynomial curve fit J. M. Cimbala, January 2006, Modified September 2006 x y 0 0 1.30 0.1 0.01 1.55 0.2 0.04 1.70 0.3 0.09 1.95 0.4 0.16 2.02 0.5 0.25 2.11 0.6 0.36 2.28 0.7 0.49 2.31 0.8 0.64 2.35 0.9 0.81 2.36 1 1 2.32 Straight line for the plot: x y 0 1.51 1 2.54 SUMMARY OUTPUT Regression Statistics Multiple R 0.93 R Square 0.87 Adjusted R Square 0.86 Standard Error 0.14 Observations 11 ANOVA df SS MS F Significance F Regression 1 1.15 1.15 62.54 0 Residual 9 0.17 0.02 Total 10 1.32 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1.51 0.08 19.71 0 1.34 1.68 1.34 1.68 X Variable 1 1.02 0.13 7.91 0 0.73 1.32 0.73 1.32 Quadratic fit for the plot: x y 0 1.31 0.2 1.73 0.4 2.04 0.6 2.25 0.8 2.34 1 2.33 SUMMARY OUTPUT Regression Statistics Multiple R 1 R Square 0.99 Adjusted R Square
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Unformatted text preview: 0.99 Standard Error 0.03 Observations 11 ANOVA df SS MS F Significance F Regression 2 1.31 0.66 658.83 Residual 8 0.01 Total 10 1.32 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1.31 0.02 54.34 1.25 1.36 1.25 1.36 X Variable 1 2.38 0.11 21.29 2.12 2.64 2.12 2.64 X Variable 2-1.36 0.11-12.6-1.61-1.11-1.61-1.11 x 2 Linear Regression Analysis (use Tools-Data Analysis-Regression ): Quadratic Regression Analysis (use Tools-Data Analysis-Regression ): 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 Column C Column C Column C x y...
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This note was uploaded on 07/23/2008 for the course ME 345 taught by Professor Staff during the Spring '08 term at Pennsylvania State University, University Park.

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