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Statistics

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Applied Statistics for Engineers and Scientists: Using Microsoft Excel & Minitab
David M. Levine, Baruch College, CUNY
Patricia P. Ramsey, Fordham University
Robert K. Smidt, California Polytechnic State University, San Luis Obispo

ISBN-10: 0134888014
ISBN-13: 9780134888019

Publisher: Prentice Hall
Copyright: 2001
Format: Paper; 714 pp
Published: 09/08/2000

Suggested retail price: $73.33
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For courses in Probability and Statistics.

This applied text for engineers and scientists, written in a non-theoretical manner, focuses on underlying principles that are important to students in a wide range of disciplines. It emphasizes the interpretation of results, the presentation and evaluation of assumptions, and the discussion of what should be done if the assumptions are violated. Integration of spreadsheet and statistical software (Microsoft Excel and Minitab) as well as in-depth coverage of quality and experimental design complete this treatment of statistics.

  • Emphasis on data analysis and interpretation of computer output—From Microsoft Excel and Minitab.

    • Enables students to employ realistic data and focus on interpreting statistical results not on computations or proofs. Ex.___

  • Appendices on using Microsoft Excel and Minitab.

    • Supplies students with supplementary material within the text that explains how the software is used in the book (employing in-chapter examples) and in the workplace. Ex.___

  • Coverage of statistical topics—e.g., descriptive statistics; experimental design; regression; probability modeling; and statistical inference.

    • Teaches students not only how to use statistical tools, but why and how statistical methods are useful in a wide variety of industrial settings—consistent with recommendations of a task force of engineering statistics educators.

  • Section- and chapter-end problems—Most with multiple parts.

    • Gives students the opportunity to select and solve from among the many problems applied to realistic situations—using real data whenever possible—in various fields of engineering and the sciences. Ex.___

  • Detailed case studies.

    • Shows students the connection between learning and doing and allows them to integrate concepts across chapters.

  • “A Using Statistics” example in each chapter from engineering or the sciences.

    • Illustrates the application of at least one of the statistical methods covered in the chapter. Ex.___

  • Numerous exhibit boxes.

    • Highlights important concepts without interrupting the flow of the text. Ex.___

  • Many comment boxes.

    • <%-6>Allows students to focus on the assumptions of statistical methods. Ex.___

  • Statistical tables.

    • Serves students with explanations and illustrations. Ex.___

  • Conversational writing style—Does not require a high level of mathematical sophistication.

    • Presents students with a great deal of information related to basic principles and important concepts in a way that does not “talk down to” or intimidate them. Ex.___

  • Chapter-opening quotes from a philosopher, historical figure, well known statistician, or from literature.

    • Gives students a historical context to frame each chapter. Ex.___



1. Introduction to Statistics and Quality Improvement.

What Is Statistics? Why Study Statistics? Statistical Thinking: Understanding and Managing Variability. Variables, Types of Data, and Levels of Measurement. Operational Definitions. Sampling. Statistical and Spreadsheet Software. Introduction to Quality. A History of Quality and Productivity. Themes of Quality Management. The Connection between Quality and Statistics. Appendix 1.1: Basics of the Windows User Interface. Appendix 1.2: Introduction to Microsoft Excel. Appendix 1.3: Introduction to MINITAB.



2. Tables and Charts.

Introduction and the History of Graphics. Some Tools for Studying a Process: Process Flow Diagrams and Cause-and-Effect Diagrams. The Importance of the Time-Order Plot. Tables and Charts for Numerical Data. Checksheets and Summary Tables. Concentration Diagrams. Graphing Categorical Data. Tables and Charts for Bivariate Categorical Data. Graphical Excellence. Appendix 2.1: Using Microsoft Excel for Tables and Charts. Appendix 2.2: Using MINITAB for Tables and Charts.



3. Describing and Summarizing Data.

Introduction: What's Ahead. Measures of Central Tendency, Variation, and Shape. The Box-and-Whisker Plot. Appendix 3.1: Using Microsoft Excel for Descriptive Statistics. Appendix 3.2: Using MINITAB for Descriptive Statistics.



4. Probability and Discrete Probability Distributions.

Introduction. Some Rules of Probability. The Probability Distribution. The Binomial Distribution. The Hypergeometric Distribution. The Negative Binomial and Geometric Distributions. The Poisson Distribution. Summary and Overview. Appendix 4.1: Using Microsoft Excel for Probability and Probability Distributions. Appendix 4.2: Using MINITAB for Probability and Probability Distributions.



5. Continuous Probability Distributions and Sampling Distributions.

Introduction to Continuous Probability Distributions. The Uniform Distribution. The Normal Distribution. The Standard Normal Distribution as an Approximation to the Binomial and Poisson Distributions. The Normal Probability Plot. The Lognormal Distribution. The Exponential Distribution. The Weibull Distribution. Sampling Distribution of the Mean. Sampling Distribution of the Proportion. Summary. Appendix 5.1: Using Microsoft Excel for Continuous Probability Distributions and Sampling Distributions. Appendix 5.2: Using MINTAB for Continuous Probability Distributions and Sampling Distributions.



6. Process Control Charts I: Basic Concepts and Attribute Charts.

Introduction to Control Charts and Their Applications. Introduction to the Theory of Control Charts. Introduction to Attributes Control Charts. np and p Charts. Area of Opportunity Charts (c Charts and u Charts). Summary. Appendix 6.1: Using Microsoft Excel for Attribute Control Charts. Appendix 6.1: Using Microsoft Excel for Attribute Control Charts. Appendix 6.2: Using MINITAB for Attribute Control Charts.



7. Statistical Process Control Charts II: Variables Control Charts.

Introduction to Variables Control Charts. Rational Subgroups and Sampling Decisions. Control Charts for Central Tendency (X Charts) and Variation (R and s Charts). Control Charts for Individual Values (X Charts). Special Considerations with Variable Charts. The Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) Charts. Process Capability. Summary. Appendix 7.1: Using Microsoft Excel for Variables Control Charts. Appendix 7.2: Using MINITAB for Variables Control Charts.



8. Estimation Procedures.

Introduction. Properties of Estimators. Confidence Interval Estimation of the Mean. Confidence Interval Estimation for the Variance. Prediction Interval Estimate for a Future Individual Value. Tolerance Intervals. Confidence Interval Estimation for the Proportion. Summary. Appendix 8.1: Using Microsoft Excel for Confidence Interval Estimation. Appendix 8.2: Using MINITAB for Confidence Interval Estimation.



9. Introduction to Hypothesis Testing.

Introduction. Basic Concepts of Hypothesis-Testing. One-Sample Tests for the Mean. t Test for the Difference between the Means of Two Independent Groups. Testing for the Difference between Two Variances. The Repeated Measures or Paired t Test. Chi-Square Test for the Differences among Proportions in Two or More Groups. X2 Test of Hypothesis for the Variance or Standard Deviation (Optional Topic). Wilcoxon Rank Sum Test for the Difference between Two Medians (Optional Topic). Summary. Appendix 9.1: Using Microsoft Excel for Hypothesis Testing. Appendix 9.2: Using MINITAB for Hypothesis Testing.



10. The Design of Experiments: One Factor and Randomized Block Experiments.

Introduction and Rationale. Historical Background. The Concept of Randomization. The One-Way Analysis of Variance (ANOVA). The Randomized Block Model. Kruskal-Wallis Rank Test for Differences in c Medians (Optional Topic). Appendix 10.1: Using Microsoft Excel for the Analysis of Variance. Appendix 10.2: Using MINITAB for the Analysis of Variance.



11. The Design of Experiments: Factorial Designs.

Two-Factor Factorial Designs. Factorial Designs Involving Three or More Factors. The Fractional Factorial Design. The Taguchi Approach. Summary and Overview. Appendix 11.1: Using Microsoft Excel for the Two-Factor Factorial Design. Appendix 11.2: Using MINITAB for the Two-Factor Factorial Designs.



12. Simple Linear Regression and Correlation.

Introduction. Types of Regression Models. Determining the Simple Linear Regression Equation. Measures of Variation in Regression and Correlation. Assumptions of Regression and Correlation. Residual Analysis. Inferences about the Slope. Confidence and Prediction Interval Estimation. Pitfalls in Regression and Ethical Issues. Computations in Simple Linear Regression. Correlation—Measuring the Strength of the Association. Appendix 12.1: Using Microsoft Excel for Simple Linear Regression and Correlation. Appendix 12.2: Using MINITAB for Simple Linear Regression and Correlation.



13. Multiple Regression.

Developing the Multiple-Regression Model. Residual Analysis for the Multiple-Regression Model. Testing for the Significance of the Multiple-Regression Model. Inferences Concerning the Population Regression Coefficients. Testing Portions of the Multiple-Regression Model. The Quadratic Curvilinear Regression Model. Dummy-Variable Models. Using Transformations in Regressions Models. Collinearity. Model-Building. Pitfalls in Multiple Regression. Appendix 13.1: Using Microsoft Excel for Multiple-Regression Models. Appendix 13.2: Using MINITAB for Multiple Models Regression.



Appendices.

Appendix A: Tables. Appendix B: Statistical Forms. Appendix C: Documentation for the Data Files. Appendix D: Installing the PHStat Microsoft Excel Add-In. Appendix E: Answers to Selected Odd Problems.



Index.

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