6th Edition

Statistics for Engineering and the Sciences

    1182 Pages 454 B/W Illustrations
    by Chapman & Hall

    Prepare Your Students for Statistical Work in the Real World

    Statistics for Engineering and the Sciences, Sixth Edition is designed for a two-semester introductory course on statistics for students majoring in engineering or any of the physical sciences. This popular text continues to teach students the basic concepts of data description and statistical inference as well as the statistical methods necessary for real-world applications. Students will understand how to collect and analyze data and think critically about the results.

    New to the Sixth Edition

    • Many new and updated exercises based on contemporary engineering and scientific-related studies and real data
    • More statistical software printouts and corresponding instructions for use that reflect the latest versions of the SAS, SPSS, and MINITAB software
    • Introduction of the case studies at the beginning of each chapter
    • Streamlined material on all basic sampling concepts, such as random sampling and sample survey designs, which gives students an earlier introduction to key sampling issues
    • New examples on comparing matched pairs versus independent samples, selecting the sample size for a designed experiment, and analyzing a two-factor experiment with quantitative factors
    • New section on using regression residuals to check the assumptions required in a simple linear regression analysis

    The first several chapters of the book identify the objectives of statistics, explain how to describe data, and present the basic concepts of probability. The text then introduces the two methods for making inferences about population parameters: estimation with confidence intervals and hypothesis testing. The remaining chapters extend these concepts to cover other topics useful in analyzing engineering and scientific data, including the analysis of categorical data, regression analysis, model building, analysis of variance for designed experiments, nonparametric statistics, statistical quality control, and product and system reliability.

    Introduction
    STATISTICS IN ACTION DDT Contamination of Fish in the Tennessee River
    Statistics: The Science of Data
    Fundamental Elements of Statistics
    Types of Data
    Collecting Data: Sampling
    The Role of Statistics in Critical Thinking
    A Guide to Statistical Methods Presented in This Text
    STATISTICS IN ACTION REVISITED DDT Contamination of Fish in the Tennessee River—Identifying the Data Collection Method, Population, Sample, and Types of Data

    Descriptive Statistics
    STATISTICS IN ACTION Characteristics of Contaminated Fish in the Tennessee River, Alabama
    Graphical and Numerical Methods for Describing Qualitative Data
    Graphical Methods for Describing Quantitative Data
    Numerical Methods for Describing Quantitative Data
    Measures of Central Tendency
    Measures of Variation
    Measures of Relative Standing
    Methods for Detecting Outliers
    Distorting the Truth with Descriptive Statistics
    STATISTICS IN ACTION REVISITED Characteristics of Contaminated Fish in the Tennessee River, Alabama

    Probability
    STATISTICS IN ACTION Assessing Predictors of Software Defects in NASA Spacecraft
    Instrument Code
    The Role of Probability in Statistics
    Events, Sample Spaces, and Probability
    Compound Events
    Complementary Events
    Conditional Probability
    Probability Rules for Unions and Intersections
    Bayes’ Rule (Optional)
    Some Counting Rules
    Probability and Statistics: An Example
    STATISTICS IN ACTION REVISITED Assessing Predictors of Software Defects in NASA Spacecraft Instrument Code

    Discrete Random Variables
    STATISTICS IN ACTION The Reliability of a "One-Shot" Device
    Discrete Random Variables
    The Probability Distribution for a Discrete Random Variable
    Expected Values for Random Variables
    Some Useful Expectation Theorems
    Bernoulli Trials
    The Binomial Probability Distribution
    The Multinomial Probability Distribution
    The Negative Binomial and the Geometric Probability Distributions
    The Hypergeometric Probability Distribution
    The Poisson Probability Distribution
    Moments and Moment Generating Functions (Optional)
    STATISTICS IN ACTION REVISITED The Reliability of a "One-Shot" Device

    Continuous Random Variables
    STATISTICS IN ACTION Super Weapons Development—Optimizing the Hit Ratio
    Continuous Random Variables
    The Density Function for a Continuous Random Variable
    Expected Values for Continuous Random Variables
    The Uniform Probability Distribution
    The Normal Probability Distribution
    Descriptive Methods for Assessing Normality
    Gamma-Type Probability Distributions
    The Weibull Probability Distribution
    Beta-Type Probability Distributions
    Moments and Moment Generating Functions (Optional)
    STATISTICS IN ACTION REVISTED Super Weapons Development—Optimizing the Hit Ratio

    Bivariate Probability Distributions and Sampling Distributions
    STATISTICS IN ACTION Availability of an Up/Down Maintained System
    Bivariate Probability Distributions for Discrete Random Variables
    Bivariate Probability Distributions for Continuous Random Variables
    The Expected Value of Functions of Two Random Variables
    Independence
    The Covariance and Correlation of Two Random Variables
    Probability Distributions and Expected Values of Functions of Random Variables (Optional)
    Sampling Distributions
    Approximating a Sampling Distribution by Monte Carlo Simulation
    The Sampling Distributions of Means and Sums
    Normal Approximation to the Binomial Distribution
    Sampling Distributions Related to the Normal Distribution
    STATISTICS IN ACTION REVISITED Availability of an Up/Down Maintained System

    Estimation Using Confidence Intervals
    STATISTICS IN ACTION Bursting Strength of PET Beverage Bottles
    Point Estimators and their Properties
    Finding Point Estimators: Classical Methods of Estimation
    Finding Interval Estimators: The Pivotal Method
    Estimation of a Population Mean
    Estimation of the Difference between Two Population Means: Independent Samples
    Estimation of the Difference between Two Population Means: Matched Pairs
    Estimation of a Population Proportion
    Estimation of the Difference between Two Population Proportions
    Estimation of a Population Variance
    Estimation of the Ratio of Two Population Variances
    Choosing the Sample Size
    Alternative Interval Estimation Methods: Bootstrapping and Bayesian Methods (Optional)
    STATISTICS IN ACTION REVISITED Bursting Strength of PET Beverage Bottles

    Tests of Hypotheses
    STATISTICS IN ACTION Comparing Methods for Dissolving Drug Tablets—Dissolution Method Equivalence Testing
    The Relationship between Statistical Tests of Hypotheses and Confidence Intervals
    Elements and Properties of a Statistical Test
    Finding Statistical Tests: Classical Methods
    Choosing the Null and Alternative Hypotheses
    The Observed Significance Level for a Test
    Testing a Population Mean
    Testing the Difference between Two Population Means: Independent Samples
    Testing the Difference between Two Population Means: Matched Pairs
    Testing a Population Proportion
    Testing the Difference between Two Population Proportions
    Testing a Population Variance
    Testing the Ratio of Two Population Variances
    Alternative Testing Procedures: Bootstrapping and Bayesian Methods (Optional)
    STATISTICS IN ACTION REVISITED Comparing Methods for Dissolving Drug Tablets—Dissolution Method Equivalence Testing

    Categorical Data Analysis
    STATISTICS IN ACTION The Case of the Ghoulish Transplant Tissue—Who Is Responsible for Paying Damages?
    Categorical Data and Multinomial Probabilities
    Estimating Category Probabilities in a One-Way Table
    Testing Category Probabilities in a One-Way Table
    Inferences about Category Probabilities in a Two-Way (Contingency) Table
    Contingency Tables with Fixed Marginal Totals
    Exact Tests for Independence in a Contingency Table Analysis (Optional)
    STATISTICS IN ACTION REVISITED The Case of the Ghoulish Transplant Tissue

    Simple Linear Regression
    STATISTICS IN ACTION Can Dowsers Really Detect Water?
    Regression Models
    Model Assumptions
    Estimating β0 and β1: The Method of Least Squares
    Properties of the Least-Squares Estimators
    An Estimator of σ2
    Assessing the Utility of the Model: Making Inferences about the Slope
    The Coefficients of Correlation and Determination
    Using the Model for Estimation and Prediction
    Checking the Assumptions: Residual Analysis
    A Complete Example
    A Summary of the Steps to Follow in Simple Linear Regression
    STATISTICS IN ACTION REVISITED Can Dowsers Really Detect Water?

    Multiple Regression Analysis
    STATISTICS IN ACTION Bid-Rigging in the Highway Construction Industry
    General Form of a Multiple Regression Model
    Model Assumptions
    Fitting the Model: The Method of Least Squares
    Computations Using Matrix Algebra: Estimating and Making Inferences about the Individual Parameters
    Assessing Overall Model Adequacy
    A Confidence Interval for and a Prediction Interval for a Future Value of y
    A First-Order Model with Quantitative Predictors
    An Interaction Model with Quantitative Predictors
    A Quadratic (Second-Order) Model with a Quantitative Predictor
    Regression Residuals and Outliers
    Some Pitfalls: Estimability, Multicollinearity, and Extrapolation
    A Summary of the Steps to Follow in a Multiple Regression Analysis
    STATISTICS IN ACTION REVISITED Building a Model for Road Construction Costs in a Sealed Bid Market

    Model Building
    STATISTICS IN ACTION Deregulation of the Intrastate Trucking Industry
    Introduction: Why Model Building Is Important
    The Two Types of Independent Variables: Quantitative and Qualitative
    Models with a Single Quantitative Independent Variable
    Models with Two or More Quantitative Independent Variables
    Coding Quantitative Independent Variables (Optional)
    Models with One Qualitative Independent Variable
    Models with Both Quantitative and Qualitative Independent Variables
    Tests for Comparing Nested Models
    External Model Validation (Optional)
    Stepwise Regression
    STATISTICS IN ACTION REVISITED Deregulation in the Intrastate Trucking Industry

    Principles of Experimental Design
    STATISTICS IN ACTION Anti-Corrosive Behavior of Epoxy Coatings Augmented with Zinc
    Introduction
    Experimental Design Terminology
    Controlling the Information in an Experiment
    Noise-Reducing Designs
    Volume-Increasing Designs
    Selecting the Sample Size
    The Importance of Randomization
    STATISTICS IN ACTION REVISITED Anti-Corrosive Behavior of Epoxy Coatings Augmented with Zinc

    The Analysis of Variance for Designed Experiments
    STATISTICS IN ACTION Pollutants at a Housing Development—A Case of Mishandling Small Samples
    Introduction
    The Logic behind an Analysis of Variance
    One-Factor Completely Randomized Designs
    Randomized Block Designs
    Two-Factor Factorial Experiments
    More Complex Factorial Designs (Optional)
    Nested Sampling Designs (Optional)
    Multiple Comparisons of Treatment Means
    Checking ANOVA Assumptions
    STATISTICS IN ACTION REVISTED Pollutants at a Housing Development—A Case of Mishandling Small Samples

    Nonparametric Statistics
    STATISTICS IN ACTION How Vulnerable Are New Hampshire Wells to Groundwater Contamination?
    Introduction: Distribution-Free Tests
    Testing for Location of a Single Population
    Comparing Two Populations: Independent Random Samples
    Comparing Two Populations: Matched-Pairs Design
    Comparing Three or More Populations: Completely Randomized Design
    Comparing Three or More Populations: Randomized Block Design
    Nonparametric Regression
    STATISTICS IN ACTION REVISITED How Vulnerable Are New Hampshire Wells to Groundwater Contamination?

    Statistical Process and Quality Control
    STATISTICS IN ACTION Testing Jet Fuel Additive for Safety
    Total Quality Management
    Variable Control Charts
    Control Chart for Means: x-Chart
    Control Chart for Process Variation: R-Chart
    Detecting Trends in a Control Chart: Runs Analysis
    Control Chart for Percent Defectives: p-Chart
    Control Chart for the Number of Defects per Item: c-Chart
    Tolerance Limits
    Capability Analysis (Optional)
    Acceptance Sampling for Defectives
    Other Sampling Plans (Optional)
    Evolutionary Operations (Optional)
    STATISTICS IN ACTION REVISITED Testing Jet Fuel Additive for Safety

    Product and System Reliability
    STATISTICS IN ACTION Modeling the Hazard Rate of Reinforced Concrete Bridge Deck Deterioration
    Introduction
    Failure Time Distributions
    Hazard Rates
    Life Testing: Censored Sampling
    Estimating the Parameters of an Exponential Failure Time Distribution
    Estimating the Parameters of a Weibull Failure Time Distribution
    System Reliability
    STATISTICS IN ACTION REVISITED Modeling the Hazard Rate of Reinforced Concrete Bridge Deck Deterioration

    Appendix A: Matrix Algebra
    Appendix B: Useful Statistical Tables
    Appendix C: SAS for Windows Tutorial
    Appendix D:
    MINITAB for Windows Tutorial
    Appendix E: SPSS for Windows Tutorial

    Biography

    William Mendenhall was a professor emeritus in the Statistics Department and the first chairman of the department at the University of Florida. Dr. Mendenhall published articles in top statistics journals and was a prolific author of statistics textbooks.

    Terry L. Sincich is an associate professor in the Information Systems Decision Sciences Department at the University of South Florida, where he teaches introductory statistics at the undergraduate level and advanced statistics courses at the doctoral level. He has won numerous teaching awards, including the Kahn Teaching Award and Outstanding Teacher Award. Dr. Sincich is a member of the American Statistical Association and the Decision Sciences Institute. His research interests include applied statistical analysis and statistical modeling.

    "A salient feature of this book is the clarity with which many statistical concepts have been presented. A very nice blend of theory and applications. It contains a wealth of illustrative examples and problem sets. All the important concepts have been highlighted; real-life data has been extensively used throughout the book. Students will find it very appealing and useful on their way to learning the basic statistical concepts and tools."
    —Dharam V. Chopra, Wichita State University

    "I like the problems because they are all based on engineering applications of probability and statistics. I especially like the problems at the end of chapters because students have to think more to solve them. I favor problems that require calculations because engineers are problem solvers."
    —Charles H. Reilly, University of Central Florida

    "I think this text is one of the best I have seen when it comes down to real data sets. The authors successfully included small and large real data sets from various real-world problems in engineering, mathematical sciences, and natural sciences."
    —Edward J. Danial, Morgan State University