Data from Picture [R Workbook Links IIII Stocks Text to Columns 8 Flash Fill Remove Duplicates Data Validation, v v Batch ANOVA Completely Randomized Design D E F G H 1 3 Method A Method B Method C 1164 146 126 142 121 134 143 165 158 152 Formulas Sample Size 144 149 146 128 Sample Size Sum Sample Mean Sample Variance Sample Standard Deviation Grand Mean Group Count Observation Count \begin{tabular}{|l|l|} \hline & \\ & Sample Size \\ & Sum \\ & Sample Mean \\ & Sample Variance \\ & Sample Standard Deviation \\ & Grand Mean \\ & Group Count \\ & Observation Count \\ \hline p-value & Source of Variation \\ & Treatments \\ \hline & Error \\ \hline & Total \\ \hline \end{tabular} Sum of Squares \#N/A \#N/A \#N/A \#N/A \#N/A \#N/A \#N/A \#N/A \#N/A \#N/A \#N/A Sum Sample Mean Sample Variance Sample Standard Deviation Grand Mean Group Count Observation Count Source of Variation Treatments (Methods) Error Total Level of significance Reject null? Are the means equal? \begin{tabular}{|l|l|l|} \hline Sum of Squares & df Mean Square \\ \hline \end{tabular} Level of significance Reject null? Are the means equal? The company anticipates the need to assemble several million components over the next several years. Company engineers select three potential assembly methods: Method A, Method B, and Method C. Management would like to select the method that produces the fewest number parts per 10,000 parts produced that do not meet specifications. It may also be possible that there is no statistical difference between the three methods in which case the lowest cost method will be selected for production. While all parts are checked before leaving the factory, the best method will reduce the number of parts that need to be recycled back into the production process. To test each method, six batches of 10,000 components are produced using each of the three methods. The number of components out of specification are recorded in the Microsoft Excel Online file below. Analyze the data to determine if there is any difference in the mean number of components that afe out of specificaion among the three methods. After conducting the analysis report the findings to the management team. Due to a recent change by Microsoft you will need to open the XLMiner Analysis ToolPak add-in manually from the home ribbon. Screenshot of ToolPak Open spreadsheet a. Compute the sum of squares between treatments (assembly methods). b. Compute the mean square between treatments (to 1 decimal if necessary). c. Compute the sum of squares due to error. d. Compute the mean square due to error (to 1 decimal if necessary). e. Set up the ANOVA table for this problem. Round all sum of squares to the nearest whole number. Round all Mean Squares to one decimal place. Round F to two decimal places. f. At the =0.05 level of significance, test whether the means for the three methods are equal. Calculate the value of the test statistic (to 2 decimals). The p-value is (to 4 decimals): What is your conclusion for management