Cengage Learning

Higher Education

Probability and Statistics for Engineers, 5th Edition

  • Richard L. Scheaffer University of Florida, Emeritus
  • Madhuri Mulekar University of South Alabama
  • James T. McClave
  • ISBN-10: 0534403026  |  ISBN-13: 9780534403027
  • 848 Pages
  • Previous Editions: 1995, 1990, 1986
  • © 2011 | Published
  • College Bookstore Wholesale Price = $274.50
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PROBABILITY AND STATISTICS FOR ENGINEERS provides a one-semester, calculus-based introduction to engineering statistics that focuses on making intelligent sense of real engineering data and interpreting results. Traditional topics are presented thorough an accessible modern framework that emphasizes the statistical thinking, data collection and analysis, decision-making, and process improvement skills that engineers need on a daily basis to solve real problems. The text continues to be driven by its hallmark array of engineering applications--thoroughly expanded and modernized for the 5th edition--which tackle timely, interesting, and illuminating scenarios that show students the rich context behind the concepts. Within the presentation of topics and applications the authors continually develop students’ intuition for collecting their own real data, analyzing it with the latest graphical tools, and interpreting the results with a goal of improving quality control and problem-solving process. Students will not only gain solid understanding of concepts and their real-life practicality, but will learn to become active statistical practitioners for their own future careers.

Features and Benefits

  • The text has a user-friendly and less formal empirical approach, consistently stressing quality improvement and real data collection and analysis to develop students’ intuition for actively applying concepts to their careers.
  • Examples and Exercises strongly highlight real-data engineering applications that capture the full depth and breadth of societal issues that engineers and scientists are called upon to solve.
  • Graphical techniques such as stemplots, boxplots, and scatterplots are emphasized to build data analysis skills for identifying patterns in data and assessing the underlying assumptions.
  • Use of statistical software packages is encouraged for easy handling of data when making inferences and building models. Downloadable datasets are available for all real data used in the book in native file formats for the most popular software packages.
  • All essential traditional engineering statistics topics are covered, and presented concisely and to-the-point to effectively fit within a one-semester course. Instructors have flexibility to omit optional sections or place added emphasis on some topics to suit different course types and audiences.

Table of Contents

Introduction. A model for problem solving and its application. Types of data and frequency distribution tables. Tools for describing data: Graphical methods. Graphing Categorical Data. Graphing Numerical Data. Visualizing distributions. Tool for Describing Data: Numerical measures. Measures of Center. Measures of Position. Measures of variation (or spread). Reading Computer Printouts. The effect of shifting and scaling of measurements on summary measures. Summary Measures and Decisions. The Empirical Rule. Standardized Values and z-scores. Boxplots. Detecting Outliers. Summary. Supplemental Exercises.
Introduction. Two-way table for categorical data. Time series analysis. Scatterplots: Graphical analysis of association between measurements. Correlation: Estimating the strength of linear relation. Regression: Modeling linear relationships. The Coefficient of Determination. Residual Analysis: Assessing the adequacy of the model. Transformations. Reading Computer Printout. Summary. Supplemental Exercises.
Introduction. Overview of methods of data collection. Planning and Conducting Surveys. Planning and Conducting Experiments. Completely Randomized Design. Randomized Block Design. Planning and Conducting an Observational Study. Summary. Supplemental Exercises.
Introduction. Sample space and relationships among events. Definition of probability. Counting rules useful in probability. Conditional probability and independence. Rules of probability. Odds, odds ratios, and risk ratio. Summary. Supplemental Exercises.
Introduction. Random variables and their probability distributions Expected values of random variables. The Bernoulli distribution. The Binomial distribution. The Geometric and Negative Binomial distributions. The Geometric distribution. The Negative Binomial distribution. The Poisson distribution. The hypergeometric distribution. The Moment-Generating Function. Simulating probability distributions. Summary. Supplementary Exercises.
Introduction. Continuous random variables and their probability distributions. Expected values of continuous random variables. The Uniform distribution. The exponential distribution. The Gamma distribution. The Normal distribution. The Lognormal Distribution. The Beta distribution. The Weibull distribution. Reliability. The Moment-generating Functions for Continuous Random Variables. Simulating probability distributions. Summary. Supplementary Exercises.
Introduction. Bivariate and Marginal Probability Distributions. Conditional Probability Distributions. Independent Random Variables. Expected Values of Functions of Random Variables. The Multinomial Distribution. More on the Moment-Generating Function. Conditional Expectations. Compounding and Its Applications. Summary. Supplementary Exercises.
Introduction. The sampling distributions. The sampling distribution of X (General Distribution). The sampling distribution of X (Normal Distribution). The sampling distribution of sample proportion Y/n (Large sample). The sampling distribution of S² (Normal Distribution). Sampling Distributions: The multiple-sample case. The sampling distribution of (X1 - X2). The sampling distribution of XD. The sampling distribution of (^p1 - ^p2). The sampling distribution of S²1/S²2. Control Charts. The X-Chart: Known μ and σ. The X and R-Charts: Unknown μ and σ. The X and S-Charts: Unknown μ and σ. The p-Chart. The c-chart. The u-chart. Process Capability. Summary. Supplementary Exercises.
Introduction. Point estimators and their properties. Confidence Intervals: The Single-Sample Case. Confidence Interval for μ: General Distribution. Confidence Interval for Mean: Normal Distribution. Confidence Interval for Proportion: Large sample case. Confidence interval for σ². Confidence Intervals: The Multiple Samples Case. Confidence Interval for Linear Functions of Means: General Distributions. Confidence Interval for Linear Functions of Means: Normal Distributions. Large Samples Confidence Intervals for Linear Functions of Proportions. Confidence Interval for σ²2/σ²1: Normal distribution case. Prediction Intervals. Tolerance Intervals. The Method of Maximum Likelihood. Bayes Estimators. Summary. Supplementary Exercises.
Introduction. Terminology of Hypothesis Testing. Hypothesis Testing: The Single-Sample Case. Testing for Mean: General Distributions Case. Testing a Mean: Normal distribution Case. Testing for Proportion: Large Sample Case. Testing for Variance: Normal Distribution Case. Hypothesis Testing: The Multiple-Sample Case. Testing the Difference between Two means: General Distributions Case. Testing the Difference between Two means: Normal Distributions case. Testing the difference between the means for paired samples. Testing the ratio of variances: Normal distributions case. χ² tests on Frequency data. Testing parameters of the multinomial distribution. Testing equality among Binomial parameters. Test of Independence. Goodness of Fit Tests. χ² Test. Kolmogorov-Smirnov test. Using Computer Programs to Fit Distributions. Acceptance Sampling. Acceptance Sampling by Attributes. Acceptance Sampling by Variables. Summary. Supplementary Exercises.
Introduction. Regression models with one predictor variable. The probability distribution of random error component. Making inferences about slope. Estimating slope using a confidence interval. Testing a hypothesis about slope. Connection between inference for slope and correlation coefficient. Using the simple linear model for estimation and prediction. Multiple regression analysis. Fitting the model: The least-squares approach. Estimation of error variance. Inferences in multiple regression. A test of model adequacy. Estimating and testing hypothesis about individual parameters. Using the multiple regression model for estimation and prediction. Model building: A test for portion of a model. Other regression models. Response surface method. Modeling a time trend. Logistic regression. Checking conditions and some pitfalls. Checking conditions. Some pitfalls. Reading printouts. Summary. Supplemental Exercises.
Introduction. Review of Designed Experiments. Analysis of Variance (ANOVA) Technique. Analysis of Variance for Completely Randomized Design. Relationship of ANOVA for CRD with a t test and Regression. Equivalence between a t test and an F test for CRD with 2 treatments. ANOVA for CRD and Regression Analysis. Estimation for Completely randomized design. Analysis of Variance for the Randomized Block Design. ANOVA for RBD. Relation between a Paired t test and an F test for RBD. ANOVA for RBD and Regression Analysis. Bonferroni Method for Estimation for RBD. Factorial Experiments. Analysis of variance for the Factorial Experiment. Fitting Higher Order Models. Summary. Supplemental Exercises.

What's New

  • The text’s hallmark applications are thoroughly expanded and modernized with many new problems covering additional engineering situations and more timely, modern issues. New and refreshed problems alike are backed by the most up-to-date available real datasets.
  • Data collection and analysis is given added emphasis, with more examples and activities given to further develop students’ intuition. A new chapter on obtaining data introduces the common ways of obtaining data in studies, discusses biases introduced by inaccurately applied methods, and shows how technology is used for data collection.
  • Use of hand calculations has been further scaled back in situations where computations are now more easily accomplished using a computer. Software calculation and analysis is given added emphasis throughout, with updated graphs generated in statistical software to better align with the use of technology and many more visuals overall.
  • Chapters have been thoroughly reorganized and streamlined to better align with modern courses, improve the overall readability and flow of topics, and provide better consistency and intuitiveness with how concepts introduced later in the book build on concepts introduced earlier.
  • New hands-on simulation activities have been added providing deeper exploration of key concepts.


All supplements have been updated in coordination with the main title. Select the main title's "About" tab, then select "What's New" for updates specific to title's edition.

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Instructor Supplements

Student Solutions Manual  (ISBN-10: 0538735953 | ISBN-13: 9780538735957)

Contains fully worked solutions to all odd-numbered exercises in the text.

Solution Builder  (ISBN-10: 0538736011 | ISBN-13: 9780538736015)

Offers fully worked instructor solutions to all exercises in the text in customizable online format. Adopting instructors can sign up for access at www.cengage.com/solutionbuilder.

Cengage Brain Student Solutions Manual, 1 term (6 months) Instant Access  (ISBN-10: 1133042716 | ISBN-13: 9781133042716)

List Price = $99.50  | College Bookstore Wholesale Price = $99.50

Student Supplements

Student Solutions Manual  (ISBN-10: 0538735953 | ISBN-13: 9780538735957)

Contains fully worked solutions to all odd-numbered exercises in the text.

List Price = $110.95  | CengageBrain Price = $110.95  | College Bookstore Wholesale Price = $82.00

Cengage Brain Student Solutions Manual, 1 term (6 months) Instant Access  (ISBN-10: 1133042716 | ISBN-13: 9781133042716)

List Price = $99.50  | College Bookstore Wholesale Price = $99.50

Meet the Author

Author Bio

Richard L. Scheaffer

Richard L. Scheaffer, Professor Emeritus of Statistics, University of Florida, received his Ph.D. in statistics from Florida State University. Accompanying a career of teaching, research and administration, Dr. Scheaffer has led efforts on the improvement of statistics education throughout the school and college curriculum. Co-author of five textbooks, he was one of the developers of the Quantitative Literacy Project that formed the basis of the data analysis strand in the curriculum standards of the National Council of Teachers of Mathematics. He also led the task force that developed the AP Statistics Program, for which he served as Chief Faculty Consultant. Dr. Scheaffer is a Fellow and past president of the American Statistical Association, a past chair of the Conference Board of the Mathematical Sciences, and an advisor on numerous statistics education projects.

Madhuri Mulekar

Madhuri S. Mulekar, Professor of Statistics, University of South Alabama, received her Ph.D. from Oklahoma State University. She is involved in teaching, research, and consulting activities with the faculty from the college of medicine, University of South Alabama hospitals, and Mitchell Cancer Institute. She has authored an exam-preparation book for Advanced Placement (AP) Statistics and has been involved with the AP Statistics exam since it was first administered, serving on the test development committee. She is actively involved in the American Statistical Association’s efforts for improving statistics education by serving on various national committees such as Advisory Committee on Continuing Education and ASA-MAA joint committee on undergraduate statistics. Prof. Mulekar has published many research as well as teaching related articles. Recipient of many grants, she directed undergraduate research program in statistics that was funded by NSF. Dr. Mulekar is a Fellow of American Statistical Association and a recipient of outstanding scholar award by Phi Kappa Phi, the national honor society.