Clinical depression is a recurrent illness requiring treatment and often hospitalization. Nearly 50% of people who have an episode of major depression will have a recurrence within 2-3 years. Being able to prevent the recurrence of depression in people who are at risk for the disease would go a long way to alleviate the pain and suffering of patients. During the 1980's the federal government, through the National Institutes of Health (NIH), sponsored a large clinical trial to evaluate two drugs for depression. There were 3 treatment groups. Patients received either Imipramine (Imip), Lithium (Li), or a Placebo (Pl). Researchers randomly assigned patients to one of the 3 treatment groups and followed them for 2-4 years to track any recurrences of depression. (Prien et al., Archives of General Psychiatry, 1984).Variables Hospt: Which hospital the patient was from: Labeled 1, 2, 3, 5 or 6 Treat: 0=Lithium; 1=Imipramine; 2=Placebo Outcome: 0=Success 1=Failure (recurrence of depression) Time: Number of weeks until a recurrence (if outcome=1) or until study ended (if outcome=0) AcuteT: How long the patient was depressed before the start of the current study, measured in days Age: Age in years Gender: 1=Female 2=MaleDataIf you have not done so already, download the depression datafile (as always, if a login box opens, just close it and then download the file). Then upload the file in StatCrunch. PromptWe will analyze the data to answer the second research question: Which of the drugs (if either) delayed the recurrence of depression longer relative to the placebo? In the previous lab-preparation activity, we identified Treat as the explanatory variable and Time as the response variable. We also determined that we will analyze the data using side-by-side boxplots and descriptive statistics (i.e. 5-number summaries since the graphs are boxplots). Make graphs and tables. Use StatCrunch to produce side-by-side boxplots. (directions) Embed your graphs with your initial post. Use StatCrunch to produce the descriptive statistics (a table containing the 5-number summaries for each comparison group). (directions) Copy and paste the StatCrunch output table into your initial post. Analyze the data: Compare the distributions for the treatment groups as demonstrated in Unit 2. For example, compare medians and intervals of typical values. Describe the shape and any outliers. Be sure to write your comparisons so the reader can understand the context of the numbers. For example, don't just say the median is 30; instead, say something like this: on average patients taking the placebo relapsed in 30 days (Q2=30 days). Draw a conclusion: What can we conclude from your analysis? Did one drug successfully delay a relapse of depression better than the others? What evidence supports your conclusion? Summarize your conclusions in response to both research questions: In this lab you compared three treatments (two drugs and the placebo) using two different variables. In Part 1 you compared whether or not a relapse into depression occurred for each of the two drugs and the placebo. In Part 2 you compared the length of time until the next relapse for the two drugs and the placebo. What can you conclude in light of both analyses? Is one treatment better than the other? How does the data support your conclusion?

Discuss why it is important for a person working in health care to understand statistical concepts. Provide an example of how statistical data is used in your organization or specialty area today and what you are expected to do with this information as a practitioner.

The final project for this course is the creation of a statistical analysis report. Each day, operations management professionals are faced with multiple decisions affecting various aspects of the operation. The ability to use data to drive decisions is an essential skill that is useful in any facet of an operation. The dynamic environment offers daily challenges that require the talents of the operations manager; working in this field is exciting and rewarding. Throughout the course, you will be engaged in activities that charge you with making decisions regarding inventory management, production capacity, product profitability, equipment effectiveness, and supply chain management. These are just a few of the challenges encountered in the field of operations management. The final activity in this course will provide you with the opportunity to demonstrate your ability to apply statistical tools and methods to solve a problem in a given scenario that is often encountered by an operations manager. Once you have outlined your analysis strategy and analyzed your data, you will then report your data, strategy, and overall decision that addresses the given problem. The project is divided into two milestones, which will be submitted at various points throughout the course to scaffold learning and ensure quality final submissions. These milestones will be submitted in Modules Three and Seven. The final project is due in Module Nine. In this assignment, you will demonstrate your mastery of the following course outcomes:  Apply data-based strategies in guiding a focused approach for improving operational processes  Determine the appropriate statistical methods for informing valid data-driven decision making in professional settings  Select statistical tools for guiding data-driven decision making resulting in sustainable operational processes  Utilize a structured approach for data-driven decision making for fostering continuous improvement activities  Propose operational improvement recommendations to internal and external stakeholders based on relevant data Prompt 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; outline the appropriate analysis strategy; select a suitable statistical tool; and use data analysis to ultimately drive the decision. Once this has been completed, you will be challenged to present your data, data analysis strategy, and overall decision in a concise report, justifying your analysis. Specifically, the following critical elements must be addressed: I. Introduction to the problem: A. Provide a concise description of the scenario that you will be analyzing. The following questions might help you describe the scenario: What is the type of organization identified in the scenario? What is the organization’s history and problem identified in the scenario? Who are the key internal and external stakeholders? II. Create an analysis plan to guide your analysis and decision making: A. Identify any quantifiable factors that may be affecting the performance of operational processes. Provide a concise explanation of how these factors may be affecting the operational processes. B. Develop a problem statement that addresses the given problem in the scenario and contains quantifiable measures. C. Propose a strategy that addresses the problem of the organization in the given case study and seeks to improve sustainable operational processes. How will adjustments be identified and made? 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? V. Recommend operational improvements to stakeholders: A. Summarize your analysis plan for both internal and external stakeholders. Be sure to use audience-appropriate jargon when summarizing for both groups of stakeholders. B. Explain how your decision addresses the given problem and how you reached that decision. Be sure to use audience-appropriate jargon for both groups of stakeholders. C. Justify why your decision is the best option for addressing the given problem to both internal and external stakeholders and how it will result in operational improvement. Be sure to use audience-appropriate jargon when communicating with stakeholders. Milestones Milestone One: Introduction and Analysis Plan In Module Three, you will submit your introduction and analysis plan, which are critical elements I and II. You will submit a 3- to 4-page paper that describes the scenario provided in the case study, identifies quantifiable factors that may affect operational performance, develops a problem statement, and proposes a strategy for resolving a company’s problem. This milestone will be graded with the Module One Rubric. Milestone Two: Statistical Tools and Data Analysis 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 of the appropriate statistical tools that are 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. This milestone will be graded with the Module Two Rubric. Final Project Submission: Statistical Analysis Report In Module Nine, you will submit your statistical analysis report and recommendations to management. It should be a complete, polished artifact containing all of the critical elements of the final product. It should reflect the incorporation of feedback gained throughout the course. This submission will be graded with the Final Project Rubric. Final Project Rubric Guidelines for Submission: Your statistical analysis report must be 10–12 pages in length (plus a cover page and references) and must be written in APA format. Use double spacing, 12-point Times New Roman font, and one-inch margins. Include at least six references cited in APA format.