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Probability and Statistics for Engineering and the Sciences,9780534242640
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Probability and Statistics for Engineering and the Sciences

by Jay L. DeVore
Edition:
4th
ISBN13:

9780534242640

ISBN10:
0534242642
Format:
Paperback
Pub. Date:
12/14/1994
Publisher(s):
Duxbury Press
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Summary

This best-selling text is intended for applied introductory probability and statistics courses taken by students in engineering and the physical sciences (calculus prerequisite). It emphasizes models, methodology, and applications, rather than rigorous mathematical development and theory. It features real data in exercises and examples. Also includes:
-- A chapter on joint probability distributions that introduces the concept of a statistic and its sampling distribution
-- Many real-data sets throughout
-- Strong coverage of computer-based methods, especially in analysis of variance and regression
-- Increased coverage of data analysis in Chapter One
-- Simulation experiments and bootstrapping techniques (Ch. 5)
-- Updated material on quality control

Table of Contents

CHAPTER 1 Introduction and Descriptive Statistics
1(42)
1.1 An Overview of Probability and Statistics
1(7)
1.2 Pictorial and Tabular Methods in Descriptive Statistics
8(12)
1.3 Measures of Location
20(8)
1.4 Measures of Variability
28(12)
Supplementary Exercises
40(2)
Bibliography
42(1)
CHAPTER 2 Probability
43(46)
Introduction 43(1)
2.1 Sample Spaces and Events
43(6)
2.2 Axioms, Interpretations, and Properties of Probability
49(10)
2.3 Counting Techniques
59(8)
2.4 Conditional Probability
67(11)
2.5 Independence
78(6)
Supplementary Exercises
84(4)
Bibliography
88(1)
CHAPTER 3 Discrete Random Variables and Probability Distributions
89(49)
Introduction 89(1)
3.1 Random Variables
89(5)
3.2 Probability Distributions for Discrete Random Variables
94(10)
3.3 Expected Values of Discrete Random Variables
104(9)
3.4 The Binomial Probability Distribution
113(9)
3.5 Hypergeometric and Negative Binomial Distributions
122(6)
3.6 The Poisson Probability Distribution
128(5)
Supplementary Exercises
133(4)
Bibliography
137(1)
CHAPTER 4 Continuous Random Variables and Probability Distributions
138(59)
Introduction 138(1)
4.1 Continuous Random Variables and Probability Density Functions
138(7)
4.2 Cumulative Distribution Functions and Expected Values
145(9)
4.3 The Normal Distribution
154(13)
4.4 The Gamma Distribution and Its Relatives
167(7)
4.5 Other Continuous Distributions
174(7)
4.6 Probability Plots
181(11)
Supplementary Exercises
192(4)
Bibliography
196(1)
CHAPTER 5 Joint Probability Distributions and Random Samples
197(50)
Introduction 197(1)
5.1 Jointly Distributed Random Variables
197(14)
5.2 Expected Values, Covariance, and Correlation
211(7)
5.3 Statistics and Their Distributions
218(12)
5.4 The Distribution of the Sample Mean
230(7)
5.5 The Distribution of a Linear Combination
237(6)
Supplementary Exercises
243(3)
Bibliography
246(1)
CHAPTER 6 Point Estimation
247(28)
Introduction 247(1)
6.1 Some General Concepts of Point Estimation
247(16)
6.2 Methods of Point Estimation
263(10)
Supplementary Exercises
273(1)
Bibliography
274(1)
CHAPTER 7 Statistical Intervals Based on a Single Sample
275(29)
Introduction 275(1)
7.1 Basic Properties of Confidence Intervals
276(9)
7.2 Large-Sample Confidence Intervals for a Population Mean and Proportion
285(6)
7.3 Intervals Based on a Normal Population Distribution
291(7)
7.4 Confidence Intervals for the Variance and Standard Deviation of a Normal Population
298(3)
Supplementary Exercises
301(2)
Bibliography
303(1)
CHAPTER 8 Tests of Hypotheses Based on a Single Sample
304(42)
Introduction 304(1)
8.1 Hypotheses and Test Procedures
304(11)
8.2 Tests About a Population Mean
315(12)
8.3 Tests Concerning a Population Proportion
327(6)
8.4 P-Values
333(6)
8.5 Some Comments on Selecting a Test Procedure
339(3)
Supplementary Exercises
342(3)
Bibliography
345(1)
CHAPTER 9 Inferences Based on Two Samples
346(44)
Introduction 346(1)
9.1 z Tests and Confidence Intervals for a Difference Between Two Population Means
346(11)
9.2 The Two-Sample t Test and Confidence Interval
357(9)
9.3 Analysis of Paired Data
366(9)
9.4 Inferences Concerning a Difference Between Population Proportions
375(7)
9.5 Inferences Concerning Two Population Variances
382(4)
Supplementary Exercises
386(3)
Bibliography
389(1)
CHAPTER 10 The Analysis of Variance
390(30)
Introduction 390(1)
10.1 Single-Factor ANOVA
391(10)
10.2 Multiple Comparisons in ANOVA
401(6)
10.3 More on Single-Factor ANOVA
407(10)
Supplementary Exercises
417(1)
Bibliography
418(2)
CHAPTER 11 Multifactor Analysis of Variance
420(54)
Introduction 420(1)
11.1 Two-Factor ANOVA with K(ij) = 1
421(14)
11.2 Two-Factor ANOVA with K(ij) Greater than 1
435(9)
11.3 Three-Factor ANOVA
444
11.4 2^(P) Factorial Experiments
435
Supplementary Exercises
471(2)
Bibliography
473(1)
CHAPTER 12 Simple Linear Regression and Correlation
474(49)
Introduction 474(1)
12.1 The Simple Linear Regression Model
475(5)
12.2 Estimating Model Parameters
480(15)
12.3 Inferences About the Slope Parameter Beta(1)
495(7)
12.4 Inferences Concerning (XXX)(y.x) and the Prediction of Future Y Values
502(7)
12.5 Correlation
509(10)
Supplementary Exercises
519(3)
Bibliography
522(1)
CHAPTER 13 Nonlinear and Multiple Regression
523(63)
Introduction 523(1)
13.1 Aptness of the Model and Model Checking
523(8)
13.2 Regression with Transformed Values
531(8)
13.3 Polynomial Regression
539(11)
13.4 Multiple Regression Analysis
550(18)
13.5 Other Issues in Multiple Regression
568(13)
Supplementary Exercises
581(3)
Bibliography
584(2)
CHAPTER 14 The Analysis of Categorical Data
586(36)
Introduction 586(1)
14.1 Goodness-of-Fit Tests When Category Probabilities Are Completely Specified
587(8)
14.2 Goodness of Fit for Composite Hypotheses
595(12)
14.3 Two-Way Contingency Tables
607(13)
Supplementary Exercises
620(1)
Bibliography
621(1)
CHAPTER 15 Distribution-Free Procedures
622(36)
Introduction 622(1)
15.1 The Sign Test
623(5)
15.2 The Wilcoxon Signed-Rank Test
628(10)
15.3 The Wilcoxon Rank-Sum Test
638(6)
15.4 Distribution-Free Confidence Intervals
644(7)
15.5 Distribution-Free ANOVA
651(5)
Supplementary Exercises
656(1)
Bibliography
657(1)
CHAPTER 16 Quality Control Methods
658(41)
Introduction 658(1)
16.1 General Comments on Control Charts
659(2)
16.2 Control Charts for Process Location
661(10)
16.3 Control Charts for Process Variation
671(4)
16.4 Control Charts for Attributes
675(6)
16.5 CUSUM Procedures
681(9)
16.6 Acceptance Sampling
690(7)
Supplementary Exercises
697(1)
Bibliography
698(1)
Appendix Tables
699(20)
A.1 Cumulative Binomial Probabilities
700(2)
A.2 Cumulative Poisson Probabilities
702(2)
A.3 Standard Normal Curve Areas
704(2)
A.4 The Incomplete Gamma Function
706(1)
A.5 Critical Values t(Alpha, v) for the t Distribution
707(1)
A.6 Critical Values X(Alpha^(2), v) for the Chi-Squared Distribution
708(1)
A.7 Critical Values F(Alpha, v(1), v(2)) for the F Distribution
709(2)
A.8 Critical Values Q(Alpha, m, v) for the Studentized Range Distribution
711(2)
A.9 Upper-Tail Critical Values and Probabilities for the Null Distribution of the Wilcoxon Signed-Rank Statistic S(+)
713(1)
A.10 Upper-Tail Critical Values and Probabilities for the Null Distribution of the Wilcoxon Rank-Sum Statistic W
714(1)
A.11 Critical Constant c for the Wilcoxon Signed-Rank Interval
715(1)
A.12 Critical Constant c for the Wilcoxon Rank-Sum Interval
716(1)
A.13 Curves of Beta = P (Type II Error) for t Tests
717(1)
A.14 Critical Values c(Alpha) for the Test of Normality Based on r from a Normal Probability Plot
718(1)
Answers 719(16)
Index 735


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