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Stephen Vardeman and J. Marcus Jobe's motivating new book is appropriate for students in introductory engineering statistics courses, including chemical, mechanical, environmental, civil, electrical, and industrial. The authors stress the practical issues in data collection and the interpretation of the results of statistical studies over mathematical theory. Using real data and scenario examples along with chapter-long case studies to teach readers how to apply statistical methods, the book clearly and patiently helps students learn to solve engineering problems. The book's practical, applied approach encourages students to "do" statistics by carrying data collection and analysis projects all the way from problem formulation to preparation of professional technical reports.
- Presents the material in a problem-driven fashion.
- Fosters the development of statistical thought processes by students and stresses the engineering problem-solving implications of statistical inferences.
- Features motivating examples that illustrate why engineers should learn statistics.
- Includes case studies that help students see the concepts in realistic, in-depth situations.
- Contains margin notes and annotations to help guide students.
- CD packaged with text provides Microsoft® PowerPoint visuals and audio, presenting solutions for selected section exercises.
Engineering Statistics: What and Why? / Basic Terminology / Measurement: Its Importance and Difficulty / Mathematical Models, Reality and Data Analysis
2. DATA COLLECTION
General Principles in the Collection of Engineering Data / Sampling in Enumerative Studies / Principles for Effective Experimentation / Some Common Experimental Plans / Preparing to Collect Engineering Data
3. ELEMENTARY DESCRIPTIVE STATISTICS
Elementary Graphical and Tabular Treatment of Quantitative Data / Quantiles and Related Graphical Tools / Standard Numerical Summary Measures / Descriptive Statistics for Qualitative and Count Data (Optional)
4. DESCRIBING RELATIONSHIPS BETWEEN VARIABLES
Fitting a Line by Least Squares / Fitting Curves and Surfaces by Least Squares / Fitted Effects for Factorial Data / Transformations and Choice of Measurement Scale (Optional)
5. THE PROBABILITY: THE MATHEMATICS OF RANDOMNESS
(Discrete) Random Variables / Continuous Random Variables / Probability Plotting (Optional) / Joint Distributions and Independence / Functions of Several Random Variables
6. INTRODUCTION TO FORMAL STATISTICAL INFERENCE
Large-Sample Confidence Intervals for a Mean / Large-Sample Significance Tests for a Mean / One-and Two-Sample Inference Means / One- and Two-Sample Inference for Variances / One- and Two-Sample Inference for Proportions / Prediction and Tolerance Intervals
7. INFERENCE OF UNSTRUCTURED MULTISAMPLE STUDIES
The One-Way Normal Method / Simple Confidence Intervals in Multisample Studies / Two Simultaneous Confidence Interval Methods / The One-Way Analysis of Variance (ANOVA) / Shewhart Control Charts for Measurement Data / Shewhart Control Charts for Qualitative and Count Data
8. INFERENCE FOR FULL AND FRACTIONAL FACTORIAL STUDIES
Basic Inference in Two-Way Factorials with Some Replication / p-Factor Studies with Two Levels for Each Factor / Standard Fractions of Two-Level Factorials; Part I: ½ Fractions / Standard Fractions of Two-Level Factorials; Part II: General 2"p-1" Studies
9. REGRESSION ANALYSIS-INFERENCE FOR CURVE- AND SURFACE-FITTING
Inference Methods Related to Least Squares Fitting of a Line (Simple Linear Method) / Inference Methods for General Least Squares Curve- and Surface-Fitting (Multiple Linear Regression) / Application of Multiple Regression in Response Surface Problems and Factorial Analyses / APPENDIXES / A: MORE ON PROBABILITY AND MODEL FITTING / B: TABLES / ANSWERS TO END-OF-SECTION EXERCISES / INDEX
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