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The article "Bone Density and Insertion Torque as Predictors of Anterior Cruciate Ligament Graft Fixation Strength"+ gave the accompanying data on maximum insertion torque (N. m) and yield load (N), the latter being one measure of graft strength, for 15 different specimens. Torque 1.8 2.2 1.9 1.3 2.1 2.2 1.6 2.1 1.2 1.8 2.6 2.5 2.5 1.7 1.6 Load 491 477 598 361 605 671 466 431 384 422 554 577 642 348 446 (a) Is it plausible that yield load is normally distributed? O Yes, there are more than thirty data values. Yes, a normal probability plot of yield load is quite quadratic. O Yes, a normal probability plot of yield load is quite linear. O No, there is not more than thirty data values. No, a normal probability plot of yield load is not quite linear. (b) Estimate true average yield load by calculating a confidence interval with a confidence level of 95%. (Round your answer to two decimal places.) Interpret the interval. O We are 95% confident that the true population average yield load lies above this interval. We are 95% confident that this interval contains the true average yield load. We are 95% confident that the true population average yield load lies below this interval. O We are 95% confident that this interval does not contain the true average yield load.

The article "Bone Density and Insertion Torque as Predictors of Anterior Cruciate Ligament Graft Fixation Strength"+ gave the accompanying data on maximum insertion torque (N. m) and yield load (N), the latter being one measure of graft strength, for 15 different specimens. Torque 1.8 2.2 1.9 1.3 2.1 2.2 1.6 2.1 1.2 1.8 2.6 2.5 2.5 1.7 1.6 Load 491 477 598 361 605 671 466 431 384 422 554 577 642 348 446 (a) Is it plausible that yield load is normally distributed? O Yes, there are more than thirty data values. Yes, a normal probability plot of yield load is quite quadratic. O Yes, a normal probability plot of yield load is quite linear. O No, there is not more than thirty data values. No, a normal probability plot of yield load is not quite linear. (b) Estimate true average yield load by calculating a confidence interval with a confidence level of 95%. (Round your answer to two decimal places.) Interpret the interval. O We are 95% confident that the true population average yield load lies above this interval. We are 95% confident that this interval contains the true average yield load. We are 95% confident that the true population average yield load lies below this interval. O We are 95% confident that this interval does not contain the true average yield load.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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The article "Bone Density and Insertion Torque as Predictors of Anterior Cruciate Ligament Graft Fixation Strength"+ gave the accompanying data on maximum insertion torque (Nm) and yield load (N), the latter being one measure of graft strength, for 15 different specimens. Torque 1.8 2.2 1.9 1.3 2.1 2.2 1.6 2.1 1.2 1.8 2.6 2.5 2.5 1.7 1.6 Load 491 477 598 361 605 671 466 431 384 422 554 577 642 348 446 (a) Is it plausible that yield load is normally distributed? O Yes, there are more than thirty data values. O Yes, a normal probability plot of yield load is quite quadratic. O Yes, a normal probability plot of yield load is quite linear. O No, there is not more than thirty data values. O No, a normal probability plot of yield load is not quite linear. (b) Estimate true average yield load by calculating a confidence interval with a confidence level of 95%. (Round your answer to two decimal places.) Interpret the interval. We are 95% confident that the true population average yield load lies above this interval. O We are 95% confident that this interval contains the true average yield load. O We are 95% confident that the true population average yield load lies below this interval. O We are 95% confident that this interval does not contain the true average yield load. (c) Here is output from Minitab for the regression of yield load on torque. Coef 152.44 178.23 R-Sq 53.6% Predictor Constant Torque S 73.2141 Source DF Regression 1 Residual Error 13 Total 14 SE Coef 91.17 45.97 SS 80554 69684 150238 T 1.67 3.88 R-Sq (adj) MS 80554 5360 0.118 0.002 = 50.0% F P 15.03 P 0.002
Transcribed Image Text:The article "Bone Density and Insertion Torque as Predictors of Anterior Cruciate Ligament Graft Fixation Strength"+ gave the accompanying data on maximum insertion torque (Nm) and yield load (N), the latter being one measure of graft strength, for 15 different specimens. Torque 1.8 2.2 1.9 1.3 2.1 2.2 1.6 2.1 1.2 1.8 2.6 2.5 2.5 1.7 1.6 Load 491 477 598 361 605 671 466 431 384 422 554 577 642 348 446 (a) Is it plausible that yield load is normally distributed? O Yes, there are more than thirty data values. O Yes, a normal probability plot of yield load is quite quadratic. O Yes, a normal probability plot of yield load is quite linear. O No, there is not more than thirty data values. O No, a normal probability plot of yield load is not quite linear. (b) Estimate true average yield load by calculating a confidence interval with a confidence level of 95%. (Round your answer to two decimal places.) Interpret the interval. We are 95% confident that the true population average yield load lies above this interval. O We are 95% confident that this interval contains the true average yield load. O We are 95% confident that the true population average yield load lies below this interval. O We are 95% confident that this interval does not contain the true average yield load. (c) Here is output from Minitab for the regression of yield load on torque. Coef 152.44 178.23 R-Sq 53.6% Predictor Constant Torque S 73.2141 Source DF Regression 1 Residual Error 13 Total 14 SE Coef 91.17 45.97 SS 80554 69684 150238 T 1.67 3.88 R-Sq (adj) MS 80554 5360 0.118 0.002 = 50.0% F P 15.03 P 0.002
Does the simple linear regression model specify a useful relationship between the variables? O Yes, the t-statistic and P-value are significant at any reasonable level, so we cannot conclude that a useful linear relationship exists. No, the t-statistic and P-value are not significant at any reasonable level, so we can conclude that a useful linear relationship exists. O No, the t-statistic and P-value are not significant at any reasonable level, so we cannot conclude that a useful linear relationship exists. O Yes, the t-statistic and P-value are significant at any reasonable level, so we can conclude that a useful linear relationship exists. (d) The authors of the cited paper state, "Consequently, we cannot but conclude that simple regression analysis-based methods are not clinically sufficient to predict individual fixation strength." Do you agree? [Hint: Consider predicting yield load when torque is 2.0.] Yes, prediction intervals based on this interval will be too wide to be useful. No, prediction intervals based on this interval will be wide enough to be useful. O Yes, prediction intervals based on this interval will be narrow enough to be useful. O No, prediction intervals based on this interval will be too narrow to be useful. You may need to use the appropriate table in the Appendix of Tables to answer this question.
Transcribed Image Text:Does the simple linear regression model specify a useful relationship between the variables? O Yes, the t-statistic and P-value are significant at any reasonable level, so we cannot conclude that a useful linear relationship exists. No, the t-statistic and P-value are not significant at any reasonable level, so we can conclude that a useful linear relationship exists. O No, the t-statistic and P-value are not significant at any reasonable level, so we cannot conclude that a useful linear relationship exists. O Yes, the t-statistic and P-value are significant at any reasonable level, so we can conclude that a useful linear relationship exists. (d) The authors of the cited paper state, "Consequently, we cannot but conclude that simple regression analysis-based methods are not clinically sufficient to predict individual fixation strength." Do you agree? [Hint: Consider predicting yield load when torque is 2.0.] Yes, prediction intervals based on this interval will be too wide to be useful. No, prediction intervals based on this interval will be wide enough to be useful. O Yes, prediction intervals based on this interval will be narrow enough to be useful. O No, prediction intervals based on this interval will be too narrow to be useful. You may need to use the appropriate table in the Appendix of Tables to answer this question.
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