# For the past decade, rubber powder has been used in asphalt cement to improve performance. An article includes a regression of y = axial strength (MPa) on x cube strength (MPa)based on the following sample data:112.3 97.0 92.7 86.0 102.0 99.2 95.8 103.5 89.0 86.774.7 70.7 58.0 49.2 74.3 73.0 68.3 59.5 57.3 48.9(a) Obtain the equation of the least squares line. (Round all numerical values to four decimal places.)%3DInterpret the slope.A one MPa decrease in axial strength is associated with an increase in the predicted cube strength equal to the slope.A one MPa decrease in cube strength is associated with an increase in the predicted axial strength equal to the slope.A one MPa increase in axial strength is associated with an increase in the predicted cube strength equal to the slope.A one MPa increase in cube strength is associated with an increase in the predicted axial strength equal to the slope.(b) Calculate the coefficient of determination. (Round your answer to four decimal places.)Interpret the coefficient of determination.The coefficient of determination is the proportion of the observed variation in axial strength of asphalt samples of this type that can be attributed to its linear relationshipwith cube strength.The coefficient of determination is the number of the observed samples of axial strength of asphalt that can be explained by variation in cube strength.The coefficient of determination is the proportion of the observed variation in axial strength of asphalt samples of this type that cannot be attributed to its linearrelationship with cube strength.The coefficient of determination is the pumber of the observed samples of axial strenath of asohalt that cannot be explained by vadiationMy Questions...renoth#DA0 ENG 917 PMSkypeChapter 12 - .e certified.pdf-TOSHIBAYGURENDWOME

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25 views help_outlineImage TranscriptioncloseFor the past decade, rubber powder has been used in asphalt cement to improve performance. An article includes a regression of y = axial strength (MPa) on x cube strength (MPa) based on the following sample data: 112.3 97.0 92.7 86.0 102.0 99.2 95.8 103.5 89.0 86.7 74.7 70.7 58.0 49.2 74.3 73.0 68.3 59.5 57.3 48.9 (a) Obtain the equation of the least squares line. (Round all numerical values to four decimal places.) %3D Interpret the slope. A one MPa decrease in axial strength is associated with an increase in the predicted cube strength equal to the slope. A one MPa decrease in cube strength is associated with an increase in the predicted axial strength equal to the slope. A one MPa increase in axial strength is associated with an increase in the predicted cube strength equal to the slope. A one MPa increase in cube strength is associated with an increase in the predicted axial strength equal to the slope. (b) Calculate the coefficient of determination. (Round your answer to four decimal places.) Interpret the coefficient of determination. The coefficient of determination is the proportion of the observed variation in axial strength of asphalt samples of this type that can be attributed to its linear relationship with cube strength. The coefficient of determination is the number of the observed samples of axial strength of asphalt that can be explained by variation in cube strength. The coefficient of determination is the proportion of the observed variation in axial strength of asphalt samples of this type that cannot be attributed to its linear relationship with cube strength. The coefficient of determination is the pumber of the observed samples of axial strenath of asohalt that cannot be explained by vadiation My Questions... renoth #DA0 ENG 917 PM Skype Chapter 12 - . e certified.pdf- TOSHIBA YGUR END WOME fullscreen
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Step 1

a)Regression:

In order to obtain the least square regression line, first perform regression analysis on the data.

The regression analysis is conducted here by using EXCEL. The software procedure is given below:

• Enter the data.
• Select Data > Data Analysis > Regression> OK.
• Enter Input Y Range as \$B... help_outlineImage TranscriptioncloseSUMMARY OUTPUT Regression Statistics Multiple R 0.796076019 R Square 0.633737028 Adjusted R Square 0.587954157 Standard Error 6.452617376 Observations 10 ANOVA Significance F df MS Regression 576.338832 576.338832 13.84222982 0.005867063 Residual 333.090168 41.636271 Total 909.429 t Stat P-value Coefficients Standard Error Lower 95% Upper 95% Lower 95.0% Upper 95.0% -30.03861064 25.19450888 -1.192268156 0.26731902 -88.1372523 28.06003102 -88.1372523 28.06003102 Intercept 0.968975427 0.260441229 3.720514725 0.005867063 0.368396876 1.569553977 0.368396876 1.569553977 fullscreen

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