- Series
- Pearson
- Author
- Ronald E. Walpole / Raymond Myers / Sharon L. Myers / Keying E. Ye
- Publisher
- Pearson
- Cover
- Softcover
- Edition
- 1
- Language
- English
- Total pages
- 480
- Pub.-date
- August 2013
- ISBN13
- 9781292022536
- ISBN
- 1292022531
- Related Titles

ISBN | Product | Product | Price CHF | Available | |
---|---|---|---|---|---|

Essentials of Probability & Statistics for Engineers & Scientists: Pearson New International Edition |
9781292022536 Essentials of Probability & Statistics for Engineers & Scientists: Pearson New International Edition |
85.90 | approx. 7-9 days |

**For junior/senior undergraduates taking a one-semester probability and statistics course as applied to engineering, science, or computer science.**

This text covers the essential topics needed for a fundamental understanding of basic statistics and its applications in the fields of engineering and the sciences. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. Students using this text should have the equivalent of the completion of one semester of differential and integral calculus.

- The
**balance between theory and applications**offers mathematical support to enhance coverage when necessary, giving engineers and scientists the proper mathematical context for statistical tools and methods.**Case studies**provide deeper insight into the practicality of the concepts.**Calculus**is confined to elementary probability theory and probability distributions (Chapters1–3).**Linear algebra**and the use of**matrices**are applied only in Section 7.11, where treatment of multiple linear regression and analysis of variance is covered.

**Compelling exercise sets**challenge students to use the concepts to solve problems that occur in many real-life scientific and engineering situations. Many exercises contain**real data**from studies in the fields of biomedical, bioengineering, business, computing, etc.**Real-life applications**of the Poisson, binomial, and hypergeometric distributions generate student interest using topics such as flaws in manufactured copper wire, highway potholes, hospital patient traffic, airport luggage screening, and homeland security.**Class projects**provide the opportunity for students to gather their own experimental data and draw inferences from that data. These projects illustrate the meaning of a concept or provide empirical understanding of important statistical results, and are suitable for either group or individual work.

**Statistical software**coverage in the following case studies includes SAS® and MINITAB®, with screenshots and graphics as appropriate:- Two-sample hypothesis testing
- Multiple linear regression
- Analysis of variance
- Use of two-level factorial-experiments

**Interaction plots**provide examples of scientific interpretations and new exercises using graphics.**End-of-chapter material**strengthens the connections between chapters.- “
**Pot Holes**” comments remind students of the bigger picture and how each chapter fits into that picture. These notes also discuss limitations of specific procedures and help students avoid common pitfalls in misusing statistics.

- “
**Topic outline****Chapter 1**: elementary overview of statistical inference and basic probability**Chapter 2:**random variables, probability distributions, and expectations**Chapter 3:**specific discrete and continuous distributions with illustrations of their use and relationships among them**Chapter 4:**materials on graphical methods; an important introduction to the notion of sampling distribution**Chapters 5**–**6:**one- and two- sample point and interval estimation, statistical hypothesis testing**Chapters 7**–**9**: simple and multiple linear regressions; analysis of variance; multi-factorial experiments

**1. Introduction to Statistics and Probability**

1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability

1.2 Sampling Procedures; Collection of Data

1.3 Discrete and Continuous Data.

1.4 Probability: Sample Space and Events

Exercises

1.5 Counting Sample Points

Exercises

1.6 Probability of an Event

1.7 Additive Rules

Exercises

1.8 Conditional Probability, Independence, and the Product Rule

Exercises

1.9 Bayes' Rule

Exercises

Review Exercises

**2. Random Variables, Distributions, and Expectations**

2.1 Concept of a Random Variable

2.2 Discrete Probability Distributions

2.3 Continuous Probability Distributions

Exercises

2.4 Joint Probability Distributions

Exercises

2.5 Mean of a Random Variable

Exercises

2.6 Variance and Covariance of Random Variables.

Exercises

2.7 Means and Variances of Linear Combinations of Random Variables

Exercises

Review Exercises

2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

**3. Some Probability Distributions**

3.1 Introduction and Motivation

3.2 Binomial and Multinomial Distributions

Exercises

3.3 Hypergeometric Distribution

Exercises

3.4 Negative Binomial and Geometric Distributions

3.5 Poisson Distribution and the Poisson Process

Exercises

3.6 Continuous Uniform Distribution

3.7 Normal Distribution

3.8 Areas under the Normal Curve

3.9 Applications of the Normal Distribution

Exercises

3.10 Normal Approximation to the Binomial

Exercises

3.11 Gamma and Exponential Distributions

3.12 Chi-Squared Distribution.

Exercises

Review Exercises

3.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

**4. Sampling Distributions and Data Descriptions**

4.1 Random Sampling

4.2 Some Important Statistics

Exercises

4.3 Sampling Distributions

4.4 Sampling Distribution of Means and the Central Limit Theorem

Exercises

4.5 Sampling Distribution of S2

4.6 *t*-Distribution

4.7 *F*-Distribution

4.8 Graphical Presentation

Exercises

Review Exercises

4.9 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

**5. One- and Two-Sample Estimation Problems**

5.1 Introduction

5.2 Statistical Inference

5.3 Classical Methods of Estimation.

5.4 Single Sample: Estimating the Mean

5.5 Standard Error of a Point Estimate

5.6 Prediction Intervals

5.7 Tolerance Limits

Exercises

5.8 Two Samples: Estimating the Difference between Two Means

5.9 Paired Observations

Exercises

5.10 Single Sample: Estimating a Proportion

5.11 Two Samples: Estimating the Difference between Two Proportions

Exercises

5.12 Single Sample: Estimating the Variance

Exercises

Review Exercises

5.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

**6. One- and Two-Sample Tests of Hypotheses.**

6.1 Statistical Hypotheses: General Concepts

6.2 Testing a Statistical Hypothesis

6.3 The Use of P-Values for Decision Making in Testing Hypotheses

Exercises

6.4 Single Sample: Tests Concerning a Single Mean

6.5 Two Samples: Tests on Two Means

6.6 Choice of Sample Size for Testing Means

6.7 Graphical Methods for Comparing Means

Exercises

6.8 One Sample: Test on a Single Proportion.

6.9 Two Samples: Tests on Two Proportions

Exercises

6.10 Goodness-of-Fit Test

6.11 Test for Independence (Categorical Data)

6.12 Test for Homogeneity

6.13 Two-Sample Case Study

Exercises

Review Exercises

6.14 Potential Misconceptions and Hazards;

Relationship to Material in Other Chapters

**7. One-Factor Experiments: General**

7.1 Analysis-of-Variance Technique and the Strategy of Experimental Design

7.2 One-Way Analysis of Variance (One-Way ANOVA): Completely Randomized Design

7.3 Tests for the Equality of Several Variances

Exercises

7.4 Multiple Comparisons

Exercises

7.5 Concept of Blocks and the Randomized Complete Block Design

Exercises

7.6 Random Effects Models

7.7 Case Study for One-Way Experiment

Exercises

Review Exercises

7.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

**8. Linear Regression**

8.1 Introduction to Linear Regression

8.2 The Simple Linear Regression (SLR) Model and the Least Squares Method.

Exercises

8.3 Inferences Concerning the Regression Coefficients.

8.4 Prediction

Exercises

8.5 Analysis-of-Variance Approach

8.6 Test for Linearity of Regression: Data with Repeated Observations

Exercises

8.7 Diagnostic Plots of Residuals: Graphical Detection of Violation of Assumptions

8.8 Correlation

8.9 Simple Linear Regression Case Study.

Exercises

8.10 Multiple Linear Regression and Estimation of the Coefficients

Exercises

8.11 Inferences in Multiple Linear Regression

Exercises

Review Exercises

**9. Factorial Experiments (Two or More Factors)**

9.1 Introduction

9.2 Interaction in the Two-Factor Experiment

9.3 Two-Factor Analysis of Variance

Exercises

9.4 Three-Factor Experiments.

Exercises

Review Exercises

9.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters