Statistics for Engineering and the Sciences

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 DataNumerical 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 SpacecraftInstrument Code The Role of Probability in Statistics Events, Sample Spaces, and ProbabilityCompound 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 VariablesProbability Distributions and Expected Values of Functions of Random Variables (Optional) Sampling DistributionsApproximating a Sampling Distribution by Monte Carlo SimulationThe 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 PropertiesFinding 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 TestTesting 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 ß0 and ß1: The Method of Least Squares Properties of the Least-Squares Estimators An Estimator of s2Assessing 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 VariableModels with Both Quantitative and Qualitative Independent Variables Tests for Comparing Nested Models External Model Validation (Optional) Stepwise RegressionSTATISTICS 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-

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.