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Overview
PROBABILITY AND STATISTICS FOR ENGINEERS provides a onesemester, calculusbased 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, decisionmaking, 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 applicationsthoroughly expanded and modernized for the 5th editionwhich 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 problemsolving process. Students will not only gain solid understanding of concepts and their reallife practicality, but will learn to become active statistical practitioners for their own future careers.
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Features/Benefits
 The text has a userfriendly 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 realdata 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 tothepoint to effectively fit within a onesemester course. Instructors have flexibility to omit optional sections or place added emphasis on some topics to suit different course types and audiences.
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 uptodate 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 handson simulation activities have been added providing deeper exploration of key concepts.
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ISBN10: 1111287724  ISBN13: 9781111287726
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Table of Contents
1. DATA COLLECTION AND EXPLORING UNIVARIATE DISTRIBUTIONS
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 zscores. Boxplots. Detecting Outliers. Summary. Supplemental Exercises.
2. EXPLORING BIVARIATE DISTRIBUTIONS AND ESTIMATING RELATIONS
Introduction. Twoway 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.
3. OBTAINING DATA.
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.
4. PROBABILITY.
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.
5. DISCRETE PROBABILITY DISTRIBUTIONS.
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 MomentGenerating Function. Simulating probability distributions. Summary. Supplementary Exercises.
6. CONTINUOUS PROBABILITY DISTRIBUTIONS.
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 Momentgenerating Functions for Continuous Random Variables. Simulating probability distributions. Summary. Supplementary Exercises.
7. MULTIVARIATE PROBABILITY DISTRIBUTIONS.
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 MomentGenerating Function. Conditional Expectations. Compounding and Its Applications. Summary. Supplementary Exercises.
8. STATISTICS, SAMPLING DISTRIBUTIONS, AND CONTROL CHARTS.
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 multiplesample 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 XChart: Known μ and σ. The X and RCharts: Unknown μ and σ. The X and SCharts: Unknown μ and σ. The pChart. The cchart. The uchart. Process Capability. Summary. Supplementary Exercises.
9. ESTIMATION.
Introduction. Point estimators and their properties. Confidence Intervals: The SingleSample 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.
10. HYPOTHESIS TESTING.
Introduction. Terminology of Hypothesis Testing. Hypothesis Testing: The SingleSample 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 MultipleSample 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. KolmogorovSmirnov test. Using Computer Programs to Fit Distributions. Acceptance Sampling. Acceptance Sampling by Attributes. Acceptance Sampling by Variables. Summary. Supplementary Exercises.
11. ESTIMATION AND INFERENCE FOR REGRESSION PARAMETERS.
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 leastsquares 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.
12. ANALYSIS OF VARIANCE.
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.
APPENDIX.
REFERENCES.
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All supplements have been updated in coordination with the main title. Select the main title’s "About the Solution" tab, then select "What's New" for updates specific to title's edition.
For more information about these supplements, or to obtain them, contact your Learning Consultant.
Student Solutions Manual
(ISBN10: 0538735953  ISBN13: 9780538735957)
Contains fully worked solutions to all oddnumbered exercises in the text.
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Cengage Brain Student Solutions Manual Instant Access Code
(ISBN10: 1133042716  ISBN13: 9781133042716)
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Meet the Author
About the Author
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. Coauthor 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.
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. Coauthor 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 exampreparation 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 ASAMAA 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.
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 exampreparation 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 ASAMAA 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.
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