The idea of the Project is to provide students an opportunity to select and use a tool or tools developed in this class to conduct a statistical analysis of a selected topic (which, for example, could be a process or product form their own organization or experience, or any subject of interest such as guitars, basketball, Russian folk dancing, etc.). In the Project, students are to describe the topic in detail and investigate a statistical question regarding the topic. They will then conduct an analysis of this topic and question using at least one of the techniques from this course, with the goal of developing a clearer understanding the topic and answering the statistical question posed. The result will likely not be a full in-depth analysis, which can often take months; however, applying the quantitative tools to a real-world information problem will provide insights into how organizations may use these tools to solve problems (opportunities for improvement).
INSTRUCTIONS This Project will consist of one paper that, to help ensure progress and success, can be conceived of as four "mile-marker" parts with suggested target dates for each part broken down as follows:
Final Draft (end of Wk 8): Students will provide a completed, fully APA compliant, publication-quality Word® document. This final version of the paper must include the complete analysis using the chosen tool from the course. In addition to the content notes above, the full written report should also include an interpretation of the data and the analysis. It should also include a recommendation for utilizing the study results as well as possible suggestions for further research. Body length must be at least four full and no more than ten pages, plus title, abstract, and References page(s). Graphs and charts are desirable to show the data and the results of any computations. Any other relevant supporting file(s) (such as Excel®) with any backing information such as data, formulas, or calculations should be referenced in the body of the work and should be submitted. Such additional file(s) must also exhibit high-quality but need not be publication ready as the essential information created in that application (tables, charts, etc., as applicable) will be included, referenced, and/or described in the Word® document.
Data Compil ed, By this time, students should have secured all the necessary, relevant data from their source(s) and substantially completed a rough draft of the intended final study.  This draft should include an explanation of both the data and the steps undertaken to acquire (including the reliability of the source) and process the data.  It should fully explain each variable and its importance to the topic and assessment.  There should be a minimum of two scale and one nominal variable for the data set.  The analysis should include full descriptive statistics of the variables.  Finally, it should clearly state the hypothesis being tested, describe the methodology being used, defend the selection of that methodology, fully report the testing process, and present and explain the results and implications of the statistical test run.  Example methodologies for hypothesis testing or model prediction include: 
1-Sample Test for Means (or Proportions)
2-Sample Test for Means (or Proportions)
2-Sample Test for Means of Paired Samples
Analysis of Variance (ANOVA)
Chi Square Goodness of Fit Test
Chi Square Test of Independence
Correlation Test
Multiple Regression Analysis
This draft should demonstrate the basic writing tenants of introducing, presenting, summarizing, and concluding in narrative (not outline) format.  The overall length should now be at least six pages including title sheet, three pages of body, at least one reference sheet, and at least one appendix with the raw data.  Additional desirable content will include tables and figures that document the analysis and resul ts discussed in the body. 
Business owners face many situations with outcomes that seem unpredictable. For example, your main supplier of a key batch of parts could have a lower cost, but more uncertainty in delivery time. Data and statistics can be used to concretely define and measure this uncertainty and predict when the next shipment is coming. Managerial decision-making with this statistical insight can avoid steering production, costs and   customer service into bad avenues.
Statistics are sets of mathematical equations that are used to analyze