Probability and Statistics for Engineering and the Sciences

by Jay L. DeVore

• ISBN13: 9780534242640
• ISBN10: 0534242642
• Edition: 4th
• Format: Paperback
• Copyright: 1994-12-14
• Publisher: Duxbury Press

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