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Probability and Statistics for Engineers and Scientists,9780130415295
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Probability and Statistics for Engineers and Scientists

by ; ; ;
Edition:
7th
ISBN13:

9780130415295

ISBN10:
0130415294
Format:
Hardcover
Pub. Date:
1/1/2002
Publisher(s):
PRENTICE HALL
List Price: $127.57

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Summary

This classic book provides a rigorous introduction to basic probability theory and statistical inference that is motivated by interesting, relevant applications. It assumes readers have a background in calculus, and offers a unique balance of theory and methodology.Chapter topics cover an introduction to statistics and data analysis, probability, random variables and probability distributions, mathematical expectation, some discrete probability distributions, some continuous probability distributions, functions of random variables, fundamental sampling distributions and data descriptions, one- and two-sample estimation problems, one- and two-sample tests of hypotheses, simple linear regression and correlation, multiple linear regression and certain nonlinear regression models, one factor experiments: general, factorial experiments (two or more factors), 2 k factorial experiments and fractions, nonparametric statistics, and statistical quality control.For individuals trying to apply statistical concepts to real-life, and analyze and interpret data.

Table of Contents

Preface xiii
Introduction to Statistics and Data Analysis
1(21)
Overview: Statistical Inference, Samples, Populations, and Experimental Design
1(3)
The Role of Probability
4(3)
Sampling Procedures; Collection of Data
7(2)
Measures of Location: The Sample Mean
9(2)
Measures of Variability
11(3)
Discrete and Continuous Data
14(1)
Statistical Modeling, Scientific Inspection, and Graphical Diagnostics
14(2)
Graphical Methods and Data Description
16(6)
Probability
22(41)
Sample Space
22(3)
Events
25(6)
Counting Sample Points
31(8)
Probability of an Event
39(4)
Additive Rules
43(4)
Conditional Probability
47(3)
Multiplicative Rules
50(7)
Bayes' Rule
57(6)
Review Exercises
61(2)
Random Variables and Probability Distributions
63(25)
Concept of a Random Variable
63(2)
Discrete Probability Distributions
65(4)
Continuous Probability Distributions
69(5)
Joint Probability Distributions
74(14)
Review Exercises
86(2)
Mathematical Expectation
88(27)
Mean of a Random Variable
88(7)
Variance and Covariance
95(8)
Means and Variances of Linear Combinations of Random Variables
103(7)
Chebyshev's Theorem
110(5)
Review Exercises
114(1)
Some Discrete Probability Distributions
115(27)
Introduction
115(1)
Discrete Uniform Distribution
116(1)
Binomial and Multinomial Distributions
117(9)
Hypergeometric Distribution
126(6)
Negative Binomial and Geometric Distributions
132(3)
Poisson Distribution and the Poisson Process
135(7)
Review Exercises
140(2)
Some Continuous Probability Distributions
142(35)
Continuous Uniform Distribution
142(1)
Normal Distribution
143(4)
Areas Under the Normal Curve
147(5)
Applications of the Normal Distribution
152(6)
Normal Approximation to the Binomial
158(7)
Gamma and Exponential Distributions
165(3)
Applications of the Exponential and Gamma Distributions
168(2)
Chi-Squared Distribution
170(1)
Lognormal Distribution
171(1)
Weibull Distribution
172(5)
Review Exercises
175(2)
Functions of Random Variables {Optional)
177(17)
Introduction
177(1)
Transformations of Variables
177(9)
Moments and Moment-Generating Functions
186(8)
Fundamental Sampling Distributions and Data Descriptions
194(36)
Random Sampling
194(3)
Some Important Statistics
197(4)
Data Displays and Graphical Methods
201(7)
Sampling Distributions
208(1)
Sampling Distribution of Means
209(7)
Sampling Distribution of S2
216(3)
t-Distribution
219(5)
F-Distribution
224(6)
Review Exercises
228(2)
One- and Two-Sample Estimation Problems
230(54)
Introduction
230(1)
Statistical Inference
230(1)
Classical Methods of Estimation
231(3)
Single Sample: Estimating the Mean
234(6)
Standard Error of a Point Estimate
240(1)
Prediction Interval
241(2)
Tolerance Limits
243(3)
Two Samples: Estimating the Difference Between Two Means
246(7)
Paired Observations
253(4)
Single Sample: Estimating a Proportion
257(4)
Two Samples: Estimating the Difference Between Two Proportions
261(3)
Single Sample: Estimating the Variance
264(1)
Two Samples: Estimating the Ratio of Two Variances
265(3)
Bayesian Methods of Estimation (Optional)
268(7)
Maximum Likelihood Estimation (Optional)
275(9)
Review Exercises
281(3)
One- and Two-Sample Tests of Hypotheses
284(66)
Statistical Hypotheses: General Concepts
284(2)
Testing a Statistical Hypothesis
286(8)
One- and Two-Tailed Tests
294(1)
The Use of P-Values for Decision Making
295(5)
Single Sample: Tests Concerning a Single Mean (Variance Known)
300(3)
Relationship to Confidence Interval Estimation
303(1)
Single Sample: Tests on a Single Mean (Variance Unknown)
304(3)
Two Samples: Tests on Two Means
307(5)
Choice of Sample Size for Testing Means
312(4)
Graphical Methods for Comparing Means
316(7)
One Sample: Test on a Single Proportion
323(3)
Two Samples: Tests on Two Proportions
326(2)
One- and Two-Sample Tests Concerning Variances
328(5)
Goodness-of-Fit Test
333(3)
Test for Independence (Categorical Data)
336(3)
Test for Homogeneity
339(1)
Testing for Several Proportions
340(2)
Two-Sample Case Study
342(8)
Review Exercises
347(3)
Simple Linear Regression and Correlation
350(50)
Introduction to Linear Regression
350(1)
Simple Linear Regression
351(4)
Least Squares and The Fitted Model
355(5)
Properties of the Least Squares Estimators
360(3)
Inferences Concerning the Regression Coefficients
363(5)
Prediction
368(4)
Choice of a Regression Model
372(1)
Analysis-of-Variance Approach
373(2)
Test for Linearity of Regression: Data with Repeated Observations
375(8)
Data Plots and Transformations
383(4)
Simple Linear Regression Case Study
387(3)
Correlation
390(10)
Review Exercises
396(4)
Multiple Linear Regression and Certain Nonlinear Regression Models
400(61)
Introduction
400(1)
Estimating the Coefficients
401(3)
Linear Regression Model Using Matrices (Optional)
404(7)
Properties of the Least Squares Estimators
411(2)
Inferences in Multiple Linear Regression
413(7)
Choice of a Fitted Model Through Hypothesis Testing
420(3)
Special Case of Orthogonality (Optional)
423(4)
Categorical or Indicator Variables
427(5)
Sequential Methods for Model Selection
432(6)
Study of Residuals and Violation of Assumptions
438(4)
Cross Validation, Cp, and Other Criteria for Model Selection
442(10)
Special Nonlinear Models for Nonideal Conditions
452(9)
Review Exercises
456(5)
One-Factor Experiments: General
461(58)
Analysis-of-Variance Technique
461(2)
The Strategy of Experimental Design
463(1)
One-Way Analysis of Variance: Completely Randomized Design
463(6)
Tests for the Equality Of Several Variances
469(4)
Single-Degree-of-Freedom Comparisons
473(4)
Multiple Comparisons
477(4)
Comparing Treatments with a Control
481(5)
Comparing a Set of Treatments in Blocks
486(1)
Randomized Complete Block Designs
487(7)
Graphical Methods and Further Diagnostics
494(2)
Latin Squares (Optional)
496(6)
Random Effects Models
502(5)
Power of Analysis-of-Variance Tests
507(4)
Case Study
511(8)
Review Exercises
515(4)
Factorial Experiments (Two or More Factors)
519(36)
Introduction
519(2)
Interaction and the Two-Factor Experiment
521(1)
Two-Factor Analysis of Variance
522(8)
Graphical Analysis in the Two-Factor Problem
530(5)
Three-Factor Experiments
535(10)
Model II and III Factorial Experiments
545(4)
Choice of Sample Size
549(6)
Review Exercises
552(3)
2k Factorial Experiments and Fractions
555(45)
Introduction
555(1)
Analysis of Variance and the Calculation of Effects
556(4)
Nonreplicated 2k Factorial Experiment
560(1)
Injection Molding Case Study
561(6)
Factorial Experiments in Incomplete Blocks
567(5)
Partial Confounding
572(3)
Factorial Experiments in a Regression Setting
575(4)
The Orthogonal Design
579(3)
Fractional Factorial Experiments
582(3)
Analysis of Fractional Factorial Experiments
585(4)
Higher Fractions and Screening Designs
589(1)
Construction of Resolution III and IV Designs with 8,16, and 32 Design Points
590(1)
Other Two-Level Resolution III Designs; The Plackett-Burman Designs
591(1)
Taguchi's Robust Parameter Design
592(8)
Review Exercises
598(2)
Nonparametric Statistics
600(25)
Nonparametric Tests
600(1)
Sign Test
601(4)
Signed-Rank Test
605(5)
Rank-Sum Test
610(3)
Kruskal-Wallis Test
613(3)
Runs Test
616(3)
Tolerance Limits
619(1)
Rank Correlation Coefficient
620(5)
Review Exercises
624(1)
Statistical Quality Control
625(31)
Introduction
625(2)
Nature of the Control Limits
627(1)
Purposes of the Control Chart
627(1)
Control Charts for Variables
628(15)
Control Charts for Attributes
643(6)
Cusum Control Charts
649(7)
Review Exercises
653(3)
Bibliography 656(3)
Appendix: Statistical Tables and Proofs 659(56)
Answers to Odd-Numbered Exercises 715(11)
Index 726

Excerpts

Goals, Approach and Mathematical Level The seventh edition emphasizes and illustrates the use of probabilistic models and statistical methodology that is employed in countless applications in all areas of science and engineering. There remains an important balance between theory and methodology that is featured in the text. We do not avoid the use of some theory but our goal is to let the mathematics provide insight rather than be a distraction. We feel that engineers and scientists are trained in mathematics and thus the providing of mathematical support when needed keeps the pedagogy from becoming a series of illustrated recipes in which the concepts are not understood and could never be applied or extended by the student except within very narrow bounds. The text contains an abundance of exercises in which the methodology discussed is illustrated by the use of real-life scientific scenarios and data sets. The complete set of data files which accompany the text are available for download from the text companion website, located at http://www.prenhall.com/walpole . Though we attempt to appeal to engineers, the exercises are not confined to engineering applications. The student is exposed to problems encountered in many sciences including social sciences and biomedical applications. The motivation here stems from the fact that trained engineers are more and more becoming exposed to nontraditional settings, including areas like bioinformatics and bioengineering. While we do let calculus play an important role but it should be noted that its use is confined to elementary probability theory and properties of probability distributions (Chapters 3, 4, 6, and 7). In addition, a modest amount of matrix algebra is used to support the linear regression material in Chapters 11 and 12. This is despite the fact that an "optional" section appears in Chapter 11 that includes the development of the multiple linear regression model with more substantive use of matrices. The student who uses this text should have completed one semester or two quarters of differential and integral calculus. An exposure to matrix algebra would be helpful but not necessary if the course content excludes the aforementioned optional section. Content and Course Planning The text is designed for either a one or two semester course. A reasonable curriculum for a one semester course might include Chapters 1 through 10. One may even choose to teach an early portion of Chapter 11 in order to introduce the student to the concept of simple linear regression. Chapter 1 is an overview of statistical inference, sampling and data analysis. Indeed, some very rudimentary aspects of experimental design are included, along with an appreciation of graphics and certain vital characteristics of data collection. Chapters 2, 3, and 4 deal with basic probability and discrete and continuous random variables. Chapters 5 and 6 cover specific discrete and continuous distributions with illustrations of their use and relationships among them. Chapter 7 deals with transformations of random variables. This chapter is listed as "optional" and would only be covered in a more theoretical course. This chapter is clearly the most mathematical chapter in the text. Chapter 8 includes additional material on graphical methods as well as an introduction to the notion of a sampling distribution. ThetandFdistributions are introduced along with motivation regarding their use in chapters that follow. Chapters 9 and 10 contain material on one and two sample point and interval estimation and hypothesis testing. The flexibility in a single semester course lies in the option of exclusion of Chapter 7 as well as teaching only a subset of the several specific discrete and continuous distributions discussed and illustrated in Chapters 5 and 6. There is additional flexibility involved in dealing with Chapter 9 where ma


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