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Statistics for Engineering and the SciencesBy William M. Mendenhall
M00002402
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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 analysisThe 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.
Table of Contents
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
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Author(s)
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.
Reviews
"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
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