Probability and statistics for engineers and sciences 7th edition

Curve Fitting. Analysis of Variance. Factorial Experimentation. Applications to Reliability and Life Testing. Statistical Tables. Answers to Odd-Numbered Exercises. Pearson offers affordable and accessible purchase options to meet the needs of your students. Connect with us to learn more. We're sorry! We don't recognize your username or password. Please try again.

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Out of print. If You're an Educator Download instructor resources Additional order info. Overview Features Contents Order Overview. Description For an introductory, one or two semester, sophomore-junior level course in Probability and Statistics or Applied Statistics for engineering, physical science, and mathematics students. Case studies in the first two chapters. Expanded Chapter 1 —Includes material on the distinction between sample and population, good and bad samples, and the use of a random number table to choose samples.

Exposes students to important, basic issues early. Improved standard normal distribution material —The normal table includes both negative and positive z values. Further, graphs of the normal density are used to guide students in evaluating normal probabilities. Saves the student manipulations when evaluating normal probability. Graphs of the sampling distribution showing the critical region and P value accompany the examples of testing hypotheses.

Summary tables of testing procedures. Makes for easier summary reference for students. Coverage of curve fitting —Enhanced introduction to fitting a straight line by least squares.

The role of the correlation coefficient and its own properties are highlighted. Section on graphic presentation of 22 and 23 designs —Includes coverage of blocking. Clear, concise presentation. Vast, rich collection of problem sets. Solid treatment of confidence interval techniques and hypothesis testing procedures.

Clear, current coverage of two-level factorial design. Full chapter on modern ideas of quality improvement. Provides up-to-date coverage of this popular significant trend in the field. This chapter also introduces the reader to some engineering applications of statistics, including building empirical models, designing engineering experiments, and monitoring manufacturing processes. These topics are discussed in more depth in subsequent chapters. Chapters 2, 3, 4, and 5 cover the basic concepts of probability, discrete and continuous random variables, probability distributions, expected values, joint probability distributions, and independence.

We have given a reasonably complete treatment of these topics but have avoided many of the mathematical or more theoretical details. Chapter 6 begins the treatment of statistical methods with random sampling; data summary and description techniques, including stem-and-leaf plots, histograms, box plots, and probability plotting; and several types of time series plots.

Chapter 7 discusses sampling distributions, the central limit theorem, and point estimation of parameters. This chapter also introduces some of the important properties of estimators, the method of maximum likelihood, the method of moments, and Bayesian estimation.

Chapter 8 discusses interval estimation for a single sample. Topics included are confidence intervals for means, variances or standard deviations, proportions, prediction intervals, and tolerance intervals. Chapter 9 discusses hypothesis tests for a single sample. Chapter 10 presents tests and confidence intervals for two samples. This material has been extensively rewritten and reorganized. There is detailed information and examples of methods for determining appropriate sample sizes.

We want the student to become familiar with how these techniques are used to solve real-world engineering problems and to get some understanding of the concepts behind them. We give a logical, heuristic development of the procedures rather than a formal, mathematical one. We have also included some material on nonparametric methods in these chapters.

Chapters 11 and 12 present simple and multiple linear regression including model adequacy checking and regression model diagnostics and an introduction to logistic regression.

We use matrix algebra throughout the multiple regression material Chapter 12 because it is the only easy way to understand the concepts presented. Scalar arithmetic presentations of multiple regression are awkward at best, and we have found that undergraduate engineers are exposed to enough matrix algebra to understand the presentation of this material. Chapters 13 and 14 deal with single- and multifactor experiments, respectively. The notions of randomization, blocking, factorial designs, interactions, graphical data analysis, and fractional factorials are emphasized.

Chapter 15 introduces statistical quality control, emphasizing the control chart and the fundamentals of statistical process control. Do you like this book?