7.12. Consider the putting experiment in Problem 6.21. Analyze the data considering each replicate as a...

 

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7.12. Consider the putting experiment in Problem 6.21. Analyze the data considering each replicate as a block. The analysis is similar to that of Problem 6.21. Blocking has not changed the significant factors, however, the residual plots show that the normality assumption has been violated. The transformed data also has similar analysis to the transformed data of Problem 6.21. The ANOVA shown is for the transformed data. Design Expert Output Response: Distance from cupTransform:Square root ANOVA for Selected Factorial Model Analysis of variance table [Partial sum of squares] Sum of Mean Square Source Block Model A B Residual Cor Total Squares 13.50 37.26 21.61 15.64 245.13 295.89 DF 6 2 1 1 103 111 2.25 18.63 21.61 15.64 2.38 Constant: F Value 7.83 9.08 6.57 The Model F-value of 7.83 implies the model is significant. There is only a 0.07% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B are significant model terms. Prob > F 0.0007 0.0033 0.0118 significant 7.12. Consider the putting experiment in Problem 6.21. Analyze the data considering each replicate as a block. The analysis is similar to that of Problem 6.21. Blocking has not changed the significant factors, however, the residual plots show that the normality assumption has been violated. The transformed data also has similar analysis to the transformed data of Problem 6.21. The ANOVA shown is for the transformed data. Design Expert Output Response: Distance from cupTransform:Square root ANOVA for Selected Factorial Model Analysis of variance table [Partial sum of squares] Sum of Mean Square Source Block Model A B Residual Cor Total Squares 13.50 37.26 21.61 15.64 245.13 295.89 DF 6 2 1 1 103 111 2.25 18.63 21.61 15.64 2.38 Constant: F Value 7.83 9.08 6.57 The Model F-value of 7.83 implies the model is significant. There is only a 0.07% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B are significant model terms. Prob > F 0.0007 0.0033 0.0118 significant

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The results of the analysis show that the only significant factor is the interaction ... View the full answer