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Probability and Statistics for Engineering and the Sciences, 10th Edition

Leif Ellingson, Anna Panorska

  • Copyright 2027
  • {{checkPublicationMessage('Published', '2026-01-01T00:00:00+0000')}}
Starting At £35.00 See pricing and ISBN options
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Probability and Statistics for Engineering and the Sciences 10th Edition by Leif Ellingson/Anna Panorska
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Overview

Ellingson/Panorska's "Probability and Statistics for Engineering and the Sciences," 10e takes an applied approach that helps students use probability and statistics to solve problems in engineering and science. WebAssign brings course concepts into practice through data analysis, statistical software and varied problem-solving experiences that help students move from calculation to interpretation. Updated real-world data, new R code, resampling methods and a stronger emphasis on variation connect foundational concepts to modern statistical practice.

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Meet the Authors

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Leif Ellingson

Leif Ellingson is a professor of statistics in the Department of Mathematics and Statistics at Texas Tech University. He earned his undergraduate degree in mathematics from the University of Maryland and his master’s degree and PhD in statistics from Florida State University. In addition to teaching mathematical statistics for engineers and scientists, Leif also frequently teaches introductory statistics and various graduate statistics courses. He has received multiple teaching awards from Texas Tech for his work teaching undergraduate and graduate statistics courses along with mentoring and advising graduate and undergraduate students on their research. In his spare time, Leif enjoys watching movies, reading, practicing tang soo do and above all else, spending time with his wife, Wenjing, and their son.

Anna Panorska

Anna Panorska is a professor of statistics in the Department of Mathematics and Statistics of the University of Nevada Reno. She studied applied mathematics at the University of Warsaw, Poland, and earned her MS in Statistics at the University of Texas at El Paso and PhD in Mathematics (Statistics and Applied Probability track) from the University of California at Santa Barbara. She has been developing and teaching undergraduate and graduate courses in statistics and data science for many years. She received multiple awards for her research work with undergraduate and graduate students. She has been advising and collaborating with undergraduate and graduate students in engineering and science on applying statistical methods in their disciplines. She enjoys collaborative, multidisciplinary research and developed statistical methodology used in ecology, extreme climate and weather events, medicine and finance. In her spare time Anna enjoys hiking, skiing, gardening, reading, spending time with her family and playing with her dog.

What's New

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  • New R code connects course concepts to current statistical practice, giving students hands-on experience with a widely used statistical programming language while preserving flexibility to use other software.
  • Project Milestones guide students through a longer research process, helping them practice designing, analyzing and communicating a statistical study.
  • SALT Tutorials scaffold statistical reasoning and software steps so students can focus on analyzing and interpreting data rather than navigating the tool.
  • WebAssign integrates SALT, guided R, labs and project work into the course, giving students repeated practice analyzing data and interpreting results.
  • Precalculus and calculus prerequisite exercises plus Read It, Watch It and Master It provide point-of-need support without requiring students to leave the assignment.
  • Course Packs provides a customizable homework assignment for each section, covering every learning objective with varied question types.
  • Common Error Assistance identifies format and syntax mistakes without revealing the answer, helping students correct errors and keep working.

Features

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  • WebAssign reinforces learning with a range of automatically graded exercise types, study resources, an interactive eBook and the Statistical Analysis and Learning Tool (SALT).
  • Resampling methods, including parametric and non-parametric bootstrap for estimation of the error of estimators, prepare students with current statistical methodology.
  • Revisions to Chapter 5 help students connect material from Chapters 1–4 on descriptive statistics and probability distributions to the rest of their studies in statistical methods.
  • The text emphasizes variation, particularly in a detailed description of pooled t procedures for analysis and the nature of variation in the slope estimate in simple linear regression.
  • Simulation Experiments help students understand sampling distributions and insights to gain from them, particularly when derivations are too complex to carry out.

Table of Contents

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1. OVERVIEW AND DESCRIPTIVE STATISTICS.
Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability.
2. PROBABILITY.
Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability.
Counting Techniques. Conditional Probability. Independence.
3. DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS.
Random Variables. Probability Distributions for Discrete Random Variables.
Expected Values. The Binomial Probability Distribution. Hypergeometric and Negative Binomial Distributions. The Poisson Probability Distribution.
4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS.
Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Exponential and Gamma Distributions. Other Continuous Distributions. Probability Plots.
5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES.
Jointly Distributed Random Variables. Expected Values, Covariance, and Correlation.
Statistics and Their Distributions. The Distribution of the Sample Mean. The Distribution of a Linear Combination.
6. POINT ESTIMATION.
Some General Concepts of Point Estimation. Methods of Point Estimation.
7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE.
Basic Properties of Confidence Intervals. Large-Sample Confidence Intervals for a Population Mean and Proportion. Intervals Based on a Normal Population Distribution.
Confidence Intervals for the Variance and Standard Deviation of a Normal Population.
8. TESTS OF HYPOTHESIS BASED ON A SINGLE SAMPLE.
Hypotheses and Test Procedures. z Tests for Hypotheses About a Population Mean.
The One-Sample t Test. Tests Concerning a Population Proportion. Further Aspects of Hypothesis Testing.
9. INFERENCES BASED ON TWO SAMPLES.
z Tests and Confidence Intervals for a Difference between Two Population Means.
The Two-Sample t Test and Confidence Interval. Analysis of Paired Data. Inferences Concerning a Difference between Population Proportions. Inferences Concerning Two Population Variances.
10. THE ANALYSIS OF VARIANCE.
Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA.
11. MULTIFACTOR ANALYSIS OF VARIANCE.
Two-Factor ANOVA with Kij = 1. Two-Factor ANOVA with Kij > 1. Three-Factor ANOVA. 2p Factorial Experiments.
12. SIMPLE LINEAR REGRESSION AND CORRELATION.
The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter β1. Inferences Concerning µY•x* and the Prediction of Future Y Values. Correlation.
13. NONLINEAR AND MULTIPLE REGRESSION.
Assessing Model Adequacy. Regression with Transformed Variables. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression.
14. GOODNESS-OF-FIT TESTS AND CATEGORICAL DATA ANALYSIS.
Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness-of-Fit Tests for Composite Hypotheses. Two-Way Contingency Tables.
15. DISTRIBUTION-FREE PROCEDURES.
The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA.
16. QUALITY CONTROL METHODS.
General Comments on Control Charts. Control Charts for Process Location. Control Charts for Process Variation. Control Charts for Attributes. CUSUM Procedures.
Acceptance Sampling.

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Each WebAssign online product offers the full textbook combined with a flexible and fully customizable online instructional assignments and tools.

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  • ISBN-10: 8214023947
  • ISBN-13: 9798214023946
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  • ISBN-10: 8214023939
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Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.

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ISBN: 9798214023946
WebAssign for Ellingson/Panorska's "Probability and Statistics for Engineering and the Sciences," 10th Edition, is a flexible and fully customizable online instructional solution that puts powerful tools in the hands of instructors, enabling you to deploy assignments, instantly assess individual student and class performance and help your students master the course concepts. With its powerful digital platform and content, you can tailor your course with a wide range of assignment settings, add your own questions and content and access student and course analytics and communication tools.