Posted: May 8th, 2015

Quantitative Statistics Milestone One

QSO 510: Final Project Guidelines and Rubric

 

Overview

The final project for this course is the creation of a research paper.You will select a specific problem you see in the workplace (or, if you have limited work experience, that you would find it valuable to solve in your home life) and then identify what you wish to study. You must apply the theories and concepts from the course to interpret and formulate your hypotheses.Be sure to identify why the answer/solution matters. Once you have your hypotheses, go through the scientific method and statistical process to sample and answer the question. Be sure that you justify the statistical test that you use and either reject or fail to reject the null hypothesis based on the data. Your conclusion should be entirely data driven, with the implications clear.

 

The project is divided into three 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, Five, and Seven. The final submission will occur in Module Nine.

 

Milestone 1:       The focus is on selection of a topic and identification of the tools used to solve the problem and/or test the hypothesis. If the topic is not appropriate or needs to be streamlined, the instructor will proved feedback to assure the topic can become a viable final project.

Milestone 2:       The emphasis is on the raw data and data analysis strategy that will be used to test your hypothesis and/or solve your problem. In this milestone your instructor will provide feedback to assure the raw data collected is appropriate for the topic and the data analysis strategy will support solving the problem or testing the hypothesis.

Milestone 3:       This is a draft of your final project. The instructor will provide feedback identifying the actions necessary to fulfill the requirements for the final project due in Module 9.

 

Outcomes

In this assignment, you will demonstrate your mastery of the following course outcomes:

 

• Provide students with a basic understanding of several quantitative techniques that are used extensively for decision making in business
• Enable students to recognize problem areas in their fields of professional responsibilities and to apply the appropriate quantitative methods for obtaining rational solutions
• Increase the student’s effectiveness in communicating with other specialists in the firm such as industrial engineers, production managers, operations researchers, statisticians, and other problem-solving and decision-making persons
• Enable students to use the power of the spreadsheets and statistical software in the application of the quantitative techniques

 

 

 

 

 

Prompt

Statistics is the study that pertains to the collection, analysis, explanation or interpretation, and presentation of data. Interpretation of statistical information often involves the development of a null hypothesis—that whatever is proposed as a cause has no effect on the variable being measured.

 

Specifically, the following critical elements must be addressed:

 

1. Statistical Argument: Propose an argument that answers the prompt. Include a strong thesis statement connected to data-driven evidence.
   1. Topic Selection: Select an appropriate topic and provide a detailed explanation of the significance.
   2. Citations: Paraphrase and/or integrate quotes effectively.
2. Data Collection:Once you finalize your research question, compile your research and collect raw data.
   1. Organization: Include a clearly stated thesis and a well-organized body section of your paper.
3. Statistical Process: Using your knowledge of the scientific method and statistical process to analyze the data:
   1. Descriptive Statistics: Summarize the population data by describing what was observed in the sample set numerically or graphically.
   2. Inferential Statistics: Use patterns in the sample data to draw inferences about the population represented, accounting for randomness. These inferences may take the form of hypothesis testing (e.g., answering yes/no questions about the data), estimation (estimating numerical characteristics of the data), correlation (describing associations within the data), and modeling relationships within the data.
   3. Null Hypothesis: Refer to a general or default position—that there is no relationship between two measured phenomena. Rejecting or disproving the null hypothesis is concluding that there are grounds for believing that there is a relationship between two phenomena or that a potential treatment has a measurable effect.
4. Primary-Source Analysis: Select sources in support of your thesis statement. Critically examine the sources in context of your paper topic. Remember that this is not based on opinion, but rather based on analysis of the statistical data. The source methodology supports your thesis statement.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Milestones

Milestone One: Topic Selection

In Module Three, you will submit a 2–3-page paper summarizing your topic selection. Why did you select this topic? What is the significance? Which statistical method(s) will be used? The feedback provided by the instructor should be applied to your final research paper. This milestone is graded with the Milestone One Rubric.

 

Milestone Two: Collection of Data& Data Analysis Plan

In Module Five, you will submit your raw data. You do not need your analysis to have been completed yet, but you do need your data. Make sure to present data in a well-organized spreadsheet. The feedback provided by the instructor should be applied to your final research paper.This milestone is graded with the Milestone Two Rubric.

 

Milestone Three: Rough Draft

In Module Seven, you will submit your rough draft of your data, analysis, and conclusions. List any questions or concerns you may have. In this draft, offer any analysis tools that you see as appropriate. The instructor will provide feedback. Apply this feedback to your final research paper. This milestone is graded with the Final Product Rubric (below).

 

Final Submission: Research Paper

In Module Nine, you will submit yourfinal research paper.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 is graded with the Final Product Rubric (below).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Deliverable Milestones

 

Milestone	Deliverables	Module Due	Grading
1	Topic Selection	Three	Graded separately; Milestone One Rubric
2	Collection of Data& Data Analysis Plan	Five	Graded separately; Milestone Two Rubric
3	Rough Draft	Seven	Graded separately; Final Product Rubric
	Final Submission: Research Paper	Nine	Graded separately; Final Product Rubric

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Product Rubric

Format:Your research paper should be 10 to 15 pages, plus a title page and references page. The paper should have double spacing, 12-point Times New Roman font, 1-inch margins, and APA citation style.The title page and references page will not be considered as part of the page count for this assignment.

 

Instructor Feedback: Students can find their feedback in the Grade Center or Turnitin.

 

Critical Elements	Exemplary (100%)	Proficient (90%)	Needs Improvement (70%)	Not Evident (0%)	Value
Topic Selection	Meets “Proficient” criteria and provides a substantially detailed rationale of topic’s significance	Clearly identifies the topic and provides sufficient rationale of topic’s significance	Does not clearly identify the topic; rationale is insufficient	Does not identify the topic or explain the rationale	10
Thesis	Meets “Proficient” criteria, and thesis exhibits strength and insight	Proposes a clear and complete thesis statement	Thesis is unclear and/or incomplete	Does not propose thesis statement	10
Data Collection	Meets “Proficient” criteria and supports explanation with relevant examples from research	Clearly and sufficiently explains statistical process	Explanation of statistical process is unclear and/or insufficient	Does not explain the statistical process	10
Descriptive Statistics	Meets “Proficient” criteria, and explanation is comprehensive	Clearly and sufficiently explains how data relates to descriptive statistics	Explanation of relationship between data and descriptive statistics is unclear or insufficient	Does not explain how the data relates to descriptive statistics	5
Inferential Statistics	Meets “Proficient” criteria, and explanation is comprehensive	Clearly and sufficiently explains how data relates to inferential statistics	Explanation of relationship between data and inferential statistics is unclear or insufficient	Does not explain how the data relates to inferential statistics	5
Modeling Relationships Within the Data	Meets “Proficient” criteria, and the modeling relationships chosen are appropriate, well justified, and substantially supported	Clearly illustrates modeling relationships within the data, with sufficient support	Modeling relationships are insufficient, inaccurate, and/or unsupported	Submission has no documentation of modeling relationships	10
Null Hypothesis	Meets “Proficient” criteria, and explanation is insightful and thorough	Clearly and accurately explains why the null hypothesis is rejected or not rejected	Explanation of why null hypothesis is rejected or not rejected is unclear, inaccurate, and/or incomplete	Does not explain why the null hypothesis is rejected or not rejected	10
Statistical Arguments	Meets “Proficient” criteria, and interpretation draws astute conclusions that go beyond existing research	Provides a valid and logical interpretation of statistical thought substantiated by research	Interpretation of statistical thought is invalid and/or illogical; conclusions are not substantiated by research	Does not provide an interpretation of statistical thought	10
Primary Source Analysis	Meets “Proficient” criteria and includes clear supporting details for the statistical context	Sufficiently identifies source methodology and clearly relates each to specific statistical context in which it was written	Analysis of source methodology is insufficient and/or unclear	Does not identify source methodology	10
Data Analysis	Meets “Proficient” criteria and alternative possibilities for the analysis tools chosen	Tools and the analysis plan fully defined and supported	Defined tools but it is not logical or appropriate for the plan	Does not identify tools or plan	10
Articulation of Response	Submission is free of errors related to citations, grammar, spelling, syntax, and organization and is presented in a professional and easy-to-read format	Submission has no major errors related to citations, grammar, spelling, syntax, or organization; quotes and paraphrases are effectively integrated	Submission has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and articulation of main ideas; quotes and paraphrases are awkwardly integrated	Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas	10
Earned Total Comments:					100%