spare plastics S2009) make super balls bounce even higher. They believe this new additive may even have application in the space program. For example, if the shuttle were coated in it, space junk would simply bounce off. Han and Groman have tested the performance of their S2009additive by varying the amount they mix in plastic, molding the plastic into 0.5 in. diameter balls, then measuring how high (in feet) each ball bounces when dropped from a height of 3 feet. They conducted their test on 60 different balls (one each for 60 different additive levels), and used regression analysis to estimate the relationship between bounce, the dependent variable, and S2009, the independent variable, as reflected in the following model: Bounce; =Bo + B1 S2009; +ɛ; A partial output of the regression is given below. Use this information to answer the following questions. SUMMARY OUTPUT Regression Statistics Multiple R 0.6121 K R Square Adjusted R Square Standard Error 5.4414 Observations 60 ANOVA df SS MS F Significance F Regression 34.7451 Residual 1717.3069 29.6087 Total 2746.0660 Standard Error 0.7044 0.1909 t Stat P-value Lower 95% Coefficients 1.0260 Intercept A B S2009 1.1251 D E

spare plastics S2009) make super balls bounce even higher. They believe this new additive may even have application in the space program. For example, if the shuttle were coated in it, space junk would simply bounce off. Han and Groman have tested the performance of their S2009additive by varying the amount they mix in plastic, molding the plastic into 0.5 in. diameter balls, then measuring how high (in feet) each ball bounces when dropped from a height of 3 feet. They conducted their test on 60 different balls (one each for 60 different additive levels), and used regression analysis to estimate the relationship between bounce, the dependent variable, and S2009, the independent variable, as reflected in the following model: Bounce; =Bo + B1 S2009; +ɛ; A partial output of the regression is given below. Use this information to answer the following questions. SUMMARY OUTPUT Regression Statistics Multiple R 0.6121 K R Square Adjusted R Square Standard Error 5.4414 Observations 60 ANOVA df SS MS F Significance F Regression 34.7451 Residual 1717.3069 29.6087 Total 2746.0660 Standard Error 0.7044 0.1909 t Stat P-value Lower 95% Coefficients 1.0260 Intercept A B S2009 1.1251 D E

College Algebra

10th Edition

ISBN:9781337282291

Author:Ron Larson

Publisher:Ron Larson

Chapter1: Equations, Inequalities, And Mathematical Modeling

Section: Chapter Questions

Problem 22T: On the first part of a 350-kilometer trip, a salesperson travels 2 hours and 15 minutes at an...

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