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Basic Engineering Data Collection and Analysis

ISBN-10: 053436957X
ISBN-13: 9780534369576
Edition: 2001
List price: $274.95 Buy it from $162.31
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Description: 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  More...

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Book details

List price: $274.95
Copyright year: 2001
Publisher: Brooks/Cole
Publication date: 9/19/2000
Binding: Hardcover
Pages: 848
Size: 8.25" wide x 9.25" long x 1.50" tall
Weight: 4.136
Language: English

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 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.

John Marcus Jobe currently teaches in the Department of Decision Sciences and Management Information Systems at the University of Ohio, where he teaches courses in business statistics, regression, statistical quality control, design of experiments in business, multivariate methods in business, and industrial statistics. Research focuses on quality improvement, regression, and application of designed experiments. Dr. Jobe holds a bachelor's degree from Central State University (1977), a masters of science degree from Oklahoma State University (1979), and a Ph.D. from Iowa State University (1984). He is the recipient of many distinguished research and teaching honors, including being named a Senior Fulbright Scholar, Kiev State University of Trade and Economics, Kiev, Ukraine, 1996-97. He also received the Outstanding Teaching Award, Miami University, All University Award in1995, and a NASA Research Fellowship in 1991 and 1992. In 1986 and 1987, he was recognized by the Air-Force Office of Scientific Research (AFOSR). He is the author of many papers and research articles, and served as co-author on another text with Steve Vardeman, STATISTICAL QUALITY IMPROVEMENT METHODS FOR ENGINEERS, published by John Wiley & Sons, 1998.

Introduction
Engineering Statistics: What and Why
Basic Terminology
Types of Statistical Studies
Types of Data
Types of Data Structures
Measurement: Its Importance and Difficulty
Mathematical Models, Reality, and Data Analysis
Data Collection
General Principles in the Collection of Engineering Data
Measurement
Sampling
Recording
Sampling in Enumerative Studies
Principles for Effective Experimentation
Taxonomy of Variables
Handling Extraneous Variables
Comparative Study
Replication
Allocation of Resources
Some Common Experimental Plans
Completely Randomized Experiments
Randomized Complete Block Experiments
Incomplete Block Experiments (Optional)
Preparing to Collect Engineering Data
A Series of Steps to Follow
Problem Definition
Study Definition
Physical Preparation
Elementary Descriptive Statistics
Elementary Graphical and Tabular Treatment of Quantitative Data
Dot Diagrams and Stem-and-Leaf Plots
Frequency Tables and Histograms
Scatterplots and Run Charts
Quantiles and Related Graphical Tools
Quantiles and Quantile Plots
Boxplots
Q-Q Plots and Comparing Distributional Shapes
Standard Numerical Summary Measures
Measures of Location
Measures of Spread
Statistics and Parameters
Plots of Summary Statistics
Summary Statistics and Personal Computer Software
Descriptive Statistics for Qualitative and Count Data (Optional)
Numerical Summarization of Qualitative and Count Data
Bar Charts and Plots for Qualitative and Count Data
Describing Relationships Between Variables
Fitting a Line by Least Squares
Applying the Least Squares Principle
The Sample Correlation and Coefficient of Determination
Computing and Using Residuals
Some Cautions
Computing
Fitting Curves and Surfaces by Least Squares
Curve Fitting by Least Squares
Surface Fitting by Least Squares
Some Additional Cautions
Fitted Effects for Factorial Data
Fitted Effects for 2-Factor Studies
Simpler Descriptions for Some Two-Way Data Sets
Fitted Effects for Three-Way (and Higher) Factorials
Simpler Descriptions of Some Three-Way Data Sets
Special Devices for 2[superscript p] Studies
Transformations and Choice of Measurement Scale (Optional)
Transformations and Single Samples
Transformations and Multiple Samples
Transformations and Simple Structure in Multifactor Studies
Beyond Descriptive Statistics
Probability: The Mathematics of Randomness
(Discrete) Random Variables
Random Variables and Their Distributions
Discrete Probability Functions and Cumulative Probability Functions
Summarization of Discrete Probability Distributions
The Binomial and Geometric Distributions
The Poisson Distributions
Continuous Random Variables
Probability Density Functions and Cumulative Probability Functions
Means and Variances for Continuous Distributions
The Normal Probability Distributions
The Exponential Distributions (Optional)
The Weibull Distributions (Optional)
Probability Plotting (Optional)
More on Normal Probability Plots
Probability Plots for Exponential and Weibull Distributions
Joint Distributions and Independence
Describing Jointly Discrete Random Variables
Conditional Distributions and Independence for Discrete Random Variables
Describing Jointly Continuous Random Variables (Optional)
Conditional Distributions and Independence for Continuous Random Variables (Optional)
Functions of Several Random Variables
The Distribution of a Function of Random Variables
Simulations to Approximate the Distribution of U = g(X, Y,..., Z)
Means and Variances for Linear Combinations of Random Variables
The Propagation of Error Formulas
The Central Limit Effect
Introduction to Formal Statistical Inference
Large-Sample Confidence Intervals for a Mean
A Large-n Confidence Interval for [mu] Involving [sigma]
A Generally Applicable Large-n Confidence Interval for [mu]
Some Additional Comments Concerning Confidence Intervals
Large-Sample Significance Tests for a Mean
Large-n Significance Tests for [mu] Involving [sigma]
A Five-Step Format for Summarizing Significance Tests
Generally Applicable Large-n Significance Tests for [mu]
Significance Testing and Formal Statistical Decision Making (Optional)
Some Comments Concerning Significance Testing and Estimation
One- and Two-Sample Inference for Means
Small-Sample Inference for a Single Mean
Inference for a Mean of Paired Differences
Large-Sample Comparisons of Two Means (Based on Independent Samples)
Small-Sample Comparisons of Two Means (Based on Independent Samples from Normal Distributions)
One- and Two-Sample Inference for Variances
Inference for the Variance of a Normal Distribution
Inference for the Ratio of Two Variances (Based on Independent Samples from Normal Distributions)
One- and Two-Sample Inference for Proportions
Inference for a Single Proportion
Inference for the Difference Between Two Proportions (Based on Independent Samples)
Prediction and Tolerance Intervals
Prediction Intervals for a Normal Distribution
Tolerance Intervals for a Normal Distribution
Prediction and Tolerance Intervals Based on Minimum and/or Maximum Values in a Sample
Inference for Unstructured Multisample Studies
The One-Way Normal Model
Graphical Comparison of Several Samples of Measurement Data
The One-Way (Normal) Multisample Model, Fitted Values, and Residuals
A Pooled Estimate of Variance for Multisample Studies
Standardized Residuals
Simple Confidence Intervals in Multisample Studies
Intervals for Means and for Comparing Means
Intervals for General Linear Combinations of Means
Individual and Simultaneous Confidence Levels
Two Simultaneous Confidence Interval Methods
The Pillai-Ramachandran Method
Tukey's Method
One-Way Analysis of Variance (ANOVA)
Significance Testing and Multisample Studies
The One-Way ANOVA F Test
The One-Way ANOVA Identity and Table
Random Effects Models and Analyses (Optional)
ANOVA-Based Inference for Variance Components (Optional)
Shewhart Control Charts for Measurement Data
Generalities about Shewhart Control Charts
"Standards Given" x Control Charts
Retrospective x Control Charts
Control Charts for Ranges
Control Charts for Standard Deviations
Control Charts for Measurements and Industrial Process Improvement
Shewhart Control Charts for Qualitative and Count Data
p Charts
u Charts
Common Control Chart Patterns and Special Checks
Inference for Full and Fractional Factorial Studies
Basic Inference in Two-Way Factorials with Some Replication
One-Way Methods in Two-Way Factorials
Two-Way Factorial Notation and Definitions of Effects
Individual Confidence Intervals for Factorial Effects
Tukey's Method for Comparing Main Effects (Optional)
p-Factor Studies with Two Levels for Each Factor
One-Way Methods in p-Way Factorials
p-Way Factorial Notation, Definitions of Effects, and Related Confidence Interval Methods
2[superscript p] Studies Without Replication and the Normal-Plotting of Fitted Effects
Fitting and Checking Simplified Models in Balanced 2[superscript p] Factorial Studies and a Corresponding Variance Estimate (Optional)
Confidence Intervals for Balanced 2[superscript p] Studies under Few-Effects Models (Optional)
Standard Fractions of Two-Level Factorials, Part I: 1/2 Fractions
General Observations about Fractional Factorial Studies
Choice of Standard 1/2 Fractions of 2[superscript p] Studies
Aliasing in the Standard 1/2 Fractions
Data Analysis for 2[superscript p-1] Fractional Factorials
Some Additional Comments
Standard Fractions of Two-Level Factorials Part II: General 2[superscript p-q] Studies
Using 2[superscript p-q] Fractional Factorials
Design Resolution
Two-Level Factorials and Fractional Factorials in Blocks (Optional)
Some Additional Comments
Regression Analysis--Inference for Curve- and Surface-Fitting
Inference Methods Related to the Least Squares Fitting of a Line (Simple Linear Regression)
The Simple Linear Regression Model, Corresponding Variance Estimate, and Standardized Residuals
Inference for the Slope Parameter
Inference for the Mean System Response for a Particular Value of x
Prediction and Tolerance Intervals (Optional)
Simple Linear Regression and ANOVA
Simple Linear Regression and Statistical Software
Inference Methods for General Least Squares Curve- and Surface-Fitting (Multiple Linear Regression)
The Multiple Linear Regression Model, Corresponding Variance Estimate, and Standardized Residuals
Inference for the Parameters [beta subscript 0], [beta subscript 1], [beta subscript 2],..., [beta subscript k]
Inference for the Mean System Response for a Particular Set of Values for x[subscript 1], x[subscript 2],..., x[subscript k]
Prediction and Tolerance Intervals (Optional)
Multiple Regression and ANOVA
Application of Multiple Regression in Response Surface Problems and Factorial Analyses
Surface-Fitting and Response Surface Studies
Regression and Factorial Analyses
More on Probability and Model Fitting
More Elementary Probability
Basic Definitions and Axioms
Simple Theorems of Probability Theory
Conditional Probability and the Independence of Events
Applications of Simple Probability to System Reliability Prediction
Series Systems
Parallel Systems
Combination Series-Parallel Systems
Counting
A Multiplication Principle, Permutations, and Combinations
Probabilistic Concepts Useful in Survival Analysis
Survivorship and Force-of-Mortality Functions
Maximum Likelihood Fitting of Probability Models and Related Inference Methods
Likelihood Functions for Discrete Data and Maximum Likelihood Model Fitting
Likelihood Functions for Continuous and Mixed Data and Maximum Likelihood Model Fitting
Likelihood-Based Large-Sample Inference Methods
Tables
Answers to Section Exercises
Index

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