The final project for this course is the creation of a statistical analysis report. Operations management professionals are often relied upon to make decisions regarding operational processes. Those who utilize a data-driven, structured approach have a clear advantage over those offering decisions based solely on intuition. You will be provided with a scenario often encountered by an operations manager. Your task is to review the "A-Cat Corp.: Forecasting" scenario, the addendum, and the accompanying data in the case scenario and addendum. In Module Seven, you will submit your selection of statistical tools and data analysis, which are critical elements III and IV. You will submit a 3- to 4-page paper and a spreadsheet that provides justification for the appropriate statistical tools needed to analyze the company's data, a hypothesis, the results of your analysis, any inferences from your hypothesis test, and a forecasting model that addresses the company's problem.

QSO 510 Final Project Case Addendum Vice-president Arun Mittra speculates: We have always estimated how many transformers will be needed to meet demand. The usual method is to look at the sales figures of the last two to three months and also the sales figures of the last two years in the same month. Next make a guess as to how many transformers will be needed. Either we have too many transformers in stock, or there are times when there are not enough to meet our normal production levels. It is a classic case of both understocking and overstocking. Ratnaparkhi, operations head, has been given two charges by Mittra. First, to develop an analysis of the data and present a report with recommendations. Second, \"to come up with a report that even a lower grade clerk in stores should be able to fathom and follow.\" In an effort to develop a report that is understood by all, Ratnaparkhi decides to provide incremental amounts of information to his operations manager, who is assigned the task of developing the complete analyses. A-Cat Corporation is committed to the pursuit of a robust statistical process control (quality control) program to monitor the quality of its transformers. Ratnaparkhi, aware that the construction of quality control charts depends on means and ranges, provides the following descriptive statistics for 2006 (from Exhibit 1). 2006 Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 801.1667 24.18766 793 708 83.78851 7020.515 -1.62662 0.122258 221 695 916 9614 12 The operations manager is assigned the task of developing descriptive statistics for the remaining years, 2007-2010, that are to be submitted to the quality control department. A-Cat's president asks Mittra, his vice-president of operations, to provide the sales department with an estimate of the mean number of transformers that are required to produce voltage regulators. Mittra, recalling the product data from 2006, which was the last year he supervised the production line, speculates that the mean number of transformers that are needed is less than 745 transformers. His analysis reveals the following: t = 2.32 p = .9798 This suggests that the mean number of transformers needed is not less than 745 but at least 745 transformers. Given that Mittra uses older (2006) data, his operations manager knows that he substantially underestimates current transformers requirements. She believes that the mean number of transformers required exceeds 1000 transformers and decides to test this using the most recent (2010) data. Initially, the operations manager possessed only data for years 2006 to 2008. However, she strongly believes that the mean number of transformers needed to produce voltage regulators has increased over the three-year period. She performs a one-way analysis of variance (ANOVA) analysis that follows: 2006 779 802 818 888 898 902 916 708 695 708 716 784 2007 845 739 871 927 1133 1124 1056 889 857 772 751 820 2008 857 881 937 1159 1072 1246 1198 922 798 879 945 990 Anova: Single Factor SUMMARY Groups 2006 2007 2008 Count Sum Average Variance 12 9614 801.1667 7020.515 12 10784 898.6667 18750.06 12 11884 990.3333 21117.88 ANOVA Source of Variation Between Groups Within Groups SS 214772.2 515773 Total 730545.2 df MS F P-value F crit 2 107386.1 6.870739 0.003202 3.284918 33 15629.48 35 The results (F = 6.871 and p = 0.003202) suggest that indeed the mean number of transformers has changed over the period 2006-2008. Mittra has now provided her with the remaining two years of data (2009 and 2010) and would like to know if the mean number of transformers required has changed over the period 2006-2010. Finally, the operations manager is tasked with developing a model for forecasting transformer requirements based on sales of refrigerators. The table below summarizes sales of refrigerators and transformer requirements by quarter for the period 2006-2010, which are extracted from Exhibits 2 and 1 respectively. Sales of Refrigerators 3832 5032 3947 3291 4007 5903 4274 3692 4826 6492 4765 4972 5411 7678 5774 6007 6290 8332 6107 6729 Transformer Requirements 2399 2688 2319 2208 2455 3184 2802 2343 2675 3477 2918 2814 2874 3774 3247 3107 2776 3571 3354 3513 QSO 510 Milestone Two Guidelines and Rubric The final project for this course is the creation of a statistical analysis report. Operations management professionals are often relied upon to make decisions regarding operational processes. Those who utilize a data-driven, structured approach have a clear advantage over those offering decisions based solely on intuition. You will be provided with a scenario often encountered by an operations manager. Your task is to review the \"A-Cat Corp.: Forecasting\" scenario, the addendum, and the accompanying data in the case scenario and addendum. In Module Seven, you will submit your selection of statistical tools and data analysis, which are critical elements III and IV. You will submit a 3- to 4-page paper and a spreadsheet that provides justification for the appropriate statistical tools needed to analyze the company's data, a hypothesis, the results of your analysis, any inferences from your hypothesis test, and a forecasting model that addresses the company's problem. Specifically, the following critical elements must be addressed: III. Identify statistical tools and methods to collect data: A. Identify the appropriate family of statistical tools that you will use to perform your analysis. What are your statistical assumptions concerning the data that led you to selecting this family of tools? In other words, why did you select this family of tools for statistical analysis? B. Determine the category of the provided data in the given case study. Be sure to justify why the data fits into this category type. What is the relationship between the type of data and the tools? C. From the identified family of statistical tools, select the most appropriate tool(s) for analyzing the data provided in the given case study. D. Justify why you chose this tool to analyze the data. Be sure to include how this tool will help predict the use of the data in driving decisions. E. Describe the quantitative method that will best inform data-driven decisions. Be sure to include how this method will point out the relationships between the data. How will this method allow for the most reliable data? IV. Analyze data to determine the appropriate decision for the identified problem: A. Outline the process needed to utilize your statistical analysis to reach a decision regarding the given problem. B. Explain how following this process leads to valid, data-driven decisions. In other words, why is following your outlined process important? C. After analyzing the data sets in the case study, describe the reliability of the results. Be sure to include how you know whether the results are reliable. D. Illustrate a data-driven decision that addresses the given problem. How does your decision address the given problem? How will it result in operational improvement? Guidelines for Submission: Your paper must be submitted as a 3- to 4-page Microsoft Word document and attached spreadsheet with double spacing, 12-point Times New Roman font, one-inch margins, and at least six sources cited in APA format. Instructor Feedback: This activity uses an integrated rubric in Blackboard. Students can view instructor feedback in the Grade Center. For more information, review these instructions. Rubric Critical Elements Exemplary Statistical Tools and Meets \"Proficient\" criteria and Methods: Family of identification demonstrates Statistical Tools nuanced understanding of statistical tools (100%) Statistical Tools and Methods: Category of Provided Data Statistical Tools and Methods: Most Appropriate Tool Statistical Tools and Methods: Justify Tool Statistical Tools and Methods: Quantitative Method Analyze Data: Process Proficient Identifies the appropriate family of statistical tools used to perform statistical analysis, including statistical assumptions (90%) Needs Improvement Identifies a statistical family of tools used to perform statistical analysis but either the tools are not the most appropriate to use or discussion lacks statistical assumptions (70%) Meets \"Proficient\" criteria and Determines the category of Determines the category of demonstrates insight into the the provided data, including the provided data but relationship of categorical justification to support claims category is either inaccurate data and statistical tools (90%) or discussion lacks justification (100%) to support claims (70%) Selects the most appropriate Selects a statistical tool but statistical tool used to analyze selection is not the most the data (100%) appropriate given the data (70%) Meets \"Proficient\" criteria and Justifies why the tool chosen Justifies why the tool chosen justification demonstrates is the most appropriate for is the most appropriate for the insight into the relationship analysis of this data (90%) analysis but justification is between statistical tools and either illogical or cursory type of data (100%) (70%) Meets \"Proficient\" criteria and Describes the quantitative Describes the quantitative description demonstrates method that will best inform method but either the insight into the relationship the decision, including how method selected will not between the quantitative this method will point out the result in the most reliable data method and data relationships relationships between the or discussion lacks how the (100%) data (90%) method will point out the relationships between the data (70%) Meets \"Proficient\" criteria and Outlines the process needed Outlines the process needed offers great detail for each to utilize the statistical to utilize the statistical identified step (100%) analysis (90%) analysis but steps are either inappropriate or overgeneralized (70%) Not Evident Does not determine a family of statistical tools (0%) Value 7 Does not determine a category for the data (0%) 7 Does not select a tool to be used for analysis (0%) 7 Does not justify why a particular tool was chosen (0%) 7 Does not describe the quantitative method (0%) 7 Does not outline the process needed to utilize the statistical analysis (0%) 15 Analyze Data: Valid, Meets \"Proficient\" criteria and Data-Driven explanation demonstrates a Decisions nuanced understanding of how following a process will lead to a valid decision (100%) Analyze Data: Meets \"Proficient\" criteria and Reliability of Results description demonstrates keen insight into identifying reliable data (100%) Explains how following the outlined process leads to a valid data-driven decision (90%) Explains how following the outlined process leads to a valid decision but explanation is inappropriate or cursory (70%) Describes the reliability of the Describes the reliability of the results based on data sets, results but description is including a justification to either cursory or lacks support claims (90%) justification to support claims (70%) Analyze Data: Data- Meets \"Proficient\" criteria and Illustrates a data-driven Illustrates a data-driven Driven Decision illustration demonstrates a decision that addresses the decision that addresses the deep understanding of the problem and operational problem but illustration is interplay between a problem, improvement (90%) either inappropriate or the operation, and operational overgeneralized (70%) improvement (100%) Articulation of Submission is free of errors Submission has no major Submission has major errors Response related to citations, grammar, errors related to citations, related to citations, grammar, spelling, syntax, and grammar, spelling, syntax, or spelling, syntax, or organization and is presented organization (90%) organization that negatively in a professional and easy to impact readability and read format (100%) articulation of main ideas (70%) Does not offer an explanation why following the outlined process leads to a valid decision (0%) 15 Does not describe the reliability of the results (0%) 15 Does not illustrate a decision that addresses the problem (0%) 15 Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas (0%) 5 Earned Total 100%