Understanding of several quantitative techniques

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Reference no: EM13795426

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

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.

Milestone One: Topic Selection

a 2-3-page paper summarizing your topic selection. Articulate your topic and then answer the following questions: Why did you select this topic? What is the significance? Which statistical methods will be used?

Milestone Two: Collection of Data & Data Analysis Plan

In a well-organized Excel spreadsheet, you will present the raw data collected for your research paper. You must fully support the tools chosen and the analysis that goes along with it. Along with the spreadsheet, you will include a one-page introduction that details what is being presented.

Reference no: EM13795426

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