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Working with sample data exploration and inference / Priscilla Chaffe-Stengel, Donald N. Stengel.

By: Contributor(s): Material type: TextTextSeries: Publication details: [New York, N.Y. (222 East 46th Street, New York, NY 10017) : Business Expert Press, (c)2011.Edition: first editionDescription: 1 electronic text (x, 151 pages) : illustrations, digital fileContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781606492147
Subject(s): LOC classification:
  • HF1017 .W675 2011
Online resources: Available additional physical forms:
Contents:
Chapter 1. Depicting data in telling ways -- Chapter 2. Summarizing location, scatter, and relative position -- Chapter 3. Understanding the normal distribution and the t- distribution -- Chapter 4. Using proof by contradiction to draw conclusions -- Chapter 5. Testing two population means and proportions -- Chapter 6. Analysis of variance from two or more populations -- Chapter 7. Testing proportions from two or more populations -- Chapter 8. Analyzing bivariate data -- Appendix. z-table, t-table, F table, and Chi-square table -- Index.
Abstract: Managers and analysts routinely collect and examine key performance measures to better understand their operations and make good decisions. Being able to render the complexity of operations data into a coherent account of significant events requires an understanding of how to work well with raw data and to make appropriate inferences. Although some statistical techniques for analyzing data and making inferences are sophisticated and require specialized expertise, there are methods that are understandable and applicable by anyone with basic algebra skills and the support of a spreadsheet package. By applying these fundamental methods themselves rather than turning over both the data and the responsibility for analysis and interpretation to an expert, managers will develop a richer understanding and potentially gain better control over their environment. This text is intended to describe these fundamental statistical techniques to managers, data analysts, and students. Statistical analysis of sample data is enhanced by the use of computers. Spreadsheet software is well suited for the methods discussed in this text. Examples in the text detail for the reader how to apply Microsoft Excel.
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About the authors -- Chapter 1. Depicting data in telling ways -- Chapter 2. Summarizing location, scatter, and relative position -- Chapter 3. Understanding the normal distribution and the t- distribution -- Chapter 4. Using proof by contradiction to draw conclusions -- Chapter 5. Testing two population means and proportions -- Chapter 6. Analysis of variance from two or more populations -- Chapter 7. Testing proportions from two or more populations -- Chapter 8. Analyzing bivariate data -- Appendix. z-table, t-table, F table, and Chi-square table -- Index.

Managers and analysts routinely collect and examine key performance measures to better understand their operations and make good decisions. Being able to render the complexity of operations data into a coherent account of significant events requires an understanding of how to work well with raw data and to make appropriate inferences. Although some statistical techniques for analyzing data and making inferences are sophisticated and require specialized expertise, there are methods that are understandable and applicable by anyone with basic algebra skills and the support of a spreadsheet package. By applying these fundamental methods themselves rather than turning over both the data and the responsibility for analysis and interpretation to an expert, managers will develop a richer understanding and potentially gain better control over their environment. This text is intended to describe these fundamental statistical techniques to managers, data analysts, and students. Statistical analysis of sample data is enhanced by the use of computers. Spreadsheet software is well suited for the methods discussed in this text. Examples in the text detail for the reader how to apply Microsoft Excel.

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