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Quantitative Analysis

Quantitative Analysis. Quantitative Analysis is the scientific approach to Managerial decision making. Quantitative vs. Qualitative. Quantitative = something you can measure with numbers. Qualitative = something you can describe with words. Are the students very smart?

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Quantitative Analysis

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  1. Quantitative Analysis Quantitative Analysis is the scientific approach to Managerial decision making.

  2. Quantitative vs. Qualitative Quantitative = something you can measure with numbers Qualitative = something you can describe with words • Are the students very smart? • Customer: Is this motorcycle very safe? • Is the drive between KhonKaen and Bankgok very interesting? • What did you think about the last book that you read? Because quantitative data is objective (not subjective), qualitative data is often “quantified”. For example… • How many students are there in your class? • Customer: What is the price of this motorcycle? • How many kilometers is it between KhonKaen and Bangkok? • How many books do you own? Are the students smart? With quantitative data, we can also apply many mathematical and statistical techniques to problems, in this case to managerial problems. What is the students average GPA?

  3. Managing = Being “The Boss” “Managing” can be supervising people…. …and/or supervising business activities, decisions, policies or projects.

  4. The QA Approach: Summary Feedback: Is the data OK? Does the model “work”? Feedback: Are the implications acceptable? Are the results sensitive to small changes in the model or data?

  5. 1. Define the Problem “Define” doesn’t mean getting a definition, as from a dictionary! “Defining” the problem means to articulate the problem in words, recognizing (a) objectives, (b) constraints, and (c) cross-over effects outside of problem definition.

  6. 2. Develop a Model As used here (and in all academics), a “model” is not some glamorous lady on the catwalk! A model is a simplified depiction of reality designed to depict and/or better understand reality. All models are false! Good models are useful! In QA, all models are quantitative, i.e. mathematical.

  7. 3. Acquire Input Data Quantitative input data can come from a variety of sources, such as the government, internet, customer surveys, financial statements, engineering reports, etc. A model is only as good as the quality of input data. Be careful of….. Garbage OUT Garbage IN

  8. 4. Develop a Solution MODEL Data Input Solution

  9. 5. Test the Solution Is the data OK? Does the model “work”? Does the solution make sense? Does additional data further support the data and model?

  10. 6. Analyze the Results What are the implications of the results? How robust or sensitive are the implications to small changes in the data or the model? (Sensitivity Analysis)

  11. 7. Implement the Results

  12. Computer Support for QA

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