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Statistics for Engineering and the Sciences,9780131877061
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Statistics for Engineering and the Sciences

by ;
Edition:
5th
ISBN13:

9780131877061

ISBN10:
0131877062
Format:
Paperback
Pub. Date:
7/13/2006
Publisher(s):
Pearson
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Summary

This text is designed for a two-semester introductory course in statistics for students majoring in engineering or any of the physical sciences. Inevitably, once these students graduate and are employed, they will be involved in the collection and analysis of data and will be required to think critically about the results. Consequently, they need to acquire knowledge of the basic concepts of data description and statistical inference and familiarity with statistical methods they are required to use on the job.

Table of Contents

Preface ix
Introduction
1(11)
Statistics: The Science of Data
2(1)
Fundamental Elements of Statistics
2(3)
Types of Data
5(2)
The Role of Statistics in Critical Thinking
7(2)
A Guide to Statistical Methods Presented in This Text
9(3)
Statistics In Action Contamination of Fish in the Tennessee River: Collecting the Data
10(2)
Descriptive Statistics
12(47)
Graphical and Numerical Methods for Describing Qualitative Data
13(6)
Graphical Methods for Describing Quantitative Data
19(8)
Numerical Methods for Describing Quantitative Data
27(1)
Measures of Central Tendency
28(4)
Measures of Variation
32(6)
Measures of Relative Standing
38(3)
Methods for Detecting Outliers
41(4)
Distorting the Truth with Descriptive Statistics
45(14)
Statistics in Action Characteristics of Contaminated Fish in the Tennessee River, Alabama
56(3)
Probability
59(58)
The Role of Probability in Statistics
60(1)
Events, Sample Spaces, and Probability
60(10)
Compound Events
70(2)
Complementary Events
72(4)
Conditional Probability
76(4)
Probability Rules for Unions and Intersections
80(10)
Bayes' Rule (Optional)
90(3)
Some Counting Rules
93(10)
Probability and Statistics: An Example
103(2)
Random Sampling
105(12)
Statistics in Action Assessing Predictors of Software Defects in NASA Spacecraft instrument Code
114(3)
Discrete Random Variables
117(51)
Discrete Random Variables
118(1)
The Probability Distribution for a Discrete Random Variable
118(5)
Expected Values for Random Variables
123(4)
Some Useful Expectation Theorems
127(2)
Bernoulli Trials
129(1)
The Binomial Probability Distribution
130(7)
The Multinomial Probability Distribution
137(5)
The Negative Binomial and the Geometric Probability Distributions
142(4)
The Hypergeometric Probability Distribution
146(5)
The Poisson Probability Distribution
151(6)
Moments and Moment Generating Functions (Optional)
157(11)
Statistics in Action The Reliability of a ``One-Shot'' Device
165(3)
Continuous Random Variables
168(43)
Continuous Random Variables
169(1)
The Density Function for a Continuous Random Variable
170(2)
Expected Values for Continuous Random Variables
172(5)
The Uniform Probability Distribution
177(3)
The Normal Probability Distribution
180(4)
Descriptive Methods for Assessing Normality
184(6)
Gamma-Type Probability Distributions
190(4)
The Weibull Probability Distribution
194(3)
Beta-Type Probability Distributions
197(3)
Moments and Moment Generating Functions (Optional)
200(11)
Statistics in Action Super Weapons Development---Optimizing the Hit Ratio
206(5)
Bivariate Probability Distributions and Sampling Distributions
211(51)
Bivariate Probability Distributions for Discrete Random Variables
212(5)
Bivariate Probability Distributions for Continuous Random Variables
217(4)
The Expected Value of Functions of Two Random Variables
221(2)
Independence
223(2)
The Covariance and Correlation of Two Random Variables
225(3)
Probability Distributions and Expected Values of Functions of Random Variables (Optional)
228(8)
Sampling Distributions
236(1)
Approximating a Sampling Distribution by Monte Carlo Simulation
237(3)
The Sampling Distributions of Means and Sums
240(5)
Normal Approximation to the Binomial Distribution
245(3)
Sampling Distributions Related to the Normal Distribution
248(14)
Statistics in Action Availability of an Up/Down Maintained System
259(3)
Estimation Using Confidence Intervals
262(73)
Point Estimators and their Properties
263(4)
Finding Point Estimators: Classical Methods of Estimation
267(7)
Finding Interval Estimators: The Pivotal Method
274(7)
Estimation of a Population Mean
281(6)
Estimation of the Difference Between Two Population Means: Independent Samples
287(7)
Estimation of the Difference Between Two Population Means: Matched Pairs
294(5)
Estimation of a Population Proportion
299(3)
Estimation of the Difference Between Two Population Proportions
302(3)
Estimation of a Population Variance
305(4)
Estimation of the Ratio of Two Population Variances
309(6)
Choosing the Sample Size
315(3)
Alternative Interval Estimation Methods: Bootstrapping and Bayesian Methods (Optional)
318(17)
Statistics in Action Bursting Strength of PET Beverage Bottles
332(3)
Tests of Hypotheses
335(65)
The Relationship Between Statistical Tests of Hypotheses and Confidence Intervals
336(1)
Elements and Properties of a Statistical Test
336(6)
Finding Statistical Tests: Classical Methods
342(4)
Choosing the Null and Alternative Hypotheses
346(2)
Testing a Population Mean
348(6)
The Observed Significance Level for a Test
354(3)
Testing the Difference Between Two Population Means: Independent Samples
357(7)
Testing the Difference Between Two Population Means: Matched Pairs
364(5)
Testing a Population Proportion
369(3)
Testing the Difference Between Two Population Proportions
372(4)
Testing a Population Variance
376(3)
Testing the Ratio of Two Population Variances
379(4)
Alternative Testing Procedures: Bootstrapping and Bayesian Methods (Optional)
383(17)
Statistics in Action Comparing Methods for Dissolving Drug Tablets---Dissolution Method Equivalence Testing
395(5)
Categorical Data Analysis
400(39)
Categorical Data and Multinomial Probabilities
401(1)
Estimating Category Probabilities in a One-Way Table
401(4)
Testing Category Probabilities in a One-Way Table
405(4)
Inferences About Category Probabilities in a Two-Way (Contingency) Table
409(8)
Contingency Tables with Fixed Marginal Totals
417(5)
Exact Tests for Independence in a Contingency Table Analysis (Optional)
422(17)
Statistics in Action The Public's Perception of Engineers and Engineering
432(7)
Simple Linear Regression
439(56)
Regression Models
440(1)
Model Assumptions
441(2)
Estimating β0 and β1: The Method of Least Squares
443(11)
Properties of the Least Squares Estimators
454(2)
An Estimator of σ2
456(4)
Assessing the Utility of the Model: Making Inferences About the Slope β1
460(5)
The Coefficient of Correlation
465(4)
The Coefficient of Determination
469(4)
Using the Model for Estimation and Prediction
473(7)
A Complete Example
480(3)
A Summary of the Steps to Follow in Simple Linear Regression
483(12)
Statistics in Action Can Dowsers Really Detect Water?
491(4)
Multiple Regression Analysis
495(88)
General Form of a Multiple Regression Model
496(1)
Model Assumptions
497(1)
Fitting the Model: The Method of Least Squares
498(1)
Computations Using Matrix Algebra: Estimating and Making Inferences About the Individual β Parameters
499(7)
Assessing Overall Model Adequacy
506(4)
A Confidence Interval for E(y) and a Prediction Interval for a Future Value of y
510(9)
A First-Order Model with Quantitative Predictors
519(9)
An Interaction Model with Quantitative Predictors
528(5)
A Quadratic (Second-Order) Model with a Quantitative Predictor
533(7)
Checking Assumptions: Residual Analysis
540(19)
Some Pitfalls: Estimability, Multicollinearity, and Extrapolation
559(9)
A Summary of the Steps to Follow in a Multiple Regression Analysis
568(15)
Statistics in Action Bid-Rigging in the Highway Construction Industry
575(8)
Model Building
583(65)
Introduction: Why Model Building Is Important
584(1)
The Two Types of Independent Variables: Quantitative and Qualitative
585(1)
Models with a Single Quantitative Independent Variable
586(7)
Models with Two Quantitative Independent Variables
593(7)
Coding Quantitative Independent Variables (Optional)
600(5)
Models with One Qualitative Independent Variable
605(6)
Models with Both Quantitative and Qualitative Independent Variables
611(10)
Tests for Comparing Nested Models
621(7)
External Model Validation (Optional)
628(2)
Stepwise Regression
630(18)
Statistics in Action Deregulation of the Intrastate Trucking Industry
640(8)
Principles of Experimental Design
648(23)
Introduction
649(1)
Experimental Design Terminology
649(2)
Controlling the Information in an Experiment
651(1)
Noise-Reducing Designs
652(6)
Volume-Increasing Designs
658(5)
Selecting the Sample Size
663(2)
The Importance of Randomization
665(6)
Statistics in Action Anticorrosive Behavior of Epoxy Coatings Augmented with Zinc
668(3)
The Analysis of Variance for Designed Experiments
671(84)
Introduction
672(1)
The Logic Behind an Analysis of Variance
672(2)
One-Factor Completely Randomized Designs
674(11)
Randomized Block Designs
685(13)
Two-Factor Factorial Experiments
698(16)
More Complex Factorial Designs (Optional)
714(7)
Nested Sampling Designs (Optional)
721(11)
Multiple Comparisons of Treatment Means
732(7)
Checking ANOVA Assumptions
739(16)
Statistics in Action On the Trail of the Cockroach
751(4)
Nonparametric Statistics
755(45)
Introduction: Distribution-Free Tests
756(1)
Testing for Location of a Single Population
757(5)
Comparing Two Populations: Independent Random Samples
762(7)
Comparing Two Populations: Matched-Pairs Design
769(6)
Comparing Three or More Populations: Completely Randomized Design
775(5)
Comparing Three or More Populations: Randomized Block Design
780(4)
Nonparametric Regression
784(16)
Statistics in Action Deadly Exposure: Agent Orange and Vietnam Vets
796(4)
Statistical Process and Quality Control
800(57)
Total Quality Management
801(1)
Variable Control Charts
801(5)
Control Chart for Means: x-Chart
806(8)
Control Chart for Process Variation: R-Chart
814(5)
Detecting Trends in a Control Chart: Runs Analysis
819(2)
Control Chart for Percent Defectives: p-Chart
821(4)
Control Chart for the Number of Defectives per Item: c-Chart
825(4)
Tolerance Limits
829(3)
Capability Analysis (Optional)
832(7)
Acceptance Sampling for Defectives
839(5)
Other Sampling Plans (Optional)
844(1)
Evolutionary Operations (Optional)
845(12)
Statistics in Action Testing Jet Fuel Additive for Safety
851(6)
Product and System Reliability
857(25)
Introduction
858(1)
Failure Time Distributions
858(1)
Hazard Rates
859(4)
Life Testing: Censored Sampling
863(1)
Estimating the Parameters of an Exponential Failure Time Distribution
864(3)
Estimating the Parameters of a Weibull Failure Time Distribution
867(5)
System Reliability
872(10)
Statistics in Action Modeling the Hazard Rate of Reinforced Concrete Bridge Deck Deterioration
879(3)
Appendix A Matrix Algebra
882(14)
Matrices and Matrix Multiplication
882(4)
Identity Matrices and Matrix Inversion
886(3)
Solving Systems of Simultaneous Linear Equations
889(2)
A Procedure for Inverting a Matrix
891(5)
Appendix B Useful Statistical Tables
896(40)
Table 1 Random Numbers
897(4)
Table 2 Cumulative Binomial Probabilities
901(4)
Table 3 Exponentials
905(1)
Table 4 Cumulative Poisson Probabilities
906(2)
Table 5 Normal Curve Areas
908(1)
Table 6 Gamma Function
909(1)
Table 7 Critical Values for Student's T
910(1)
Table 8 Critical Values of X2
911(2)
Table 9 Percentage Points of the F Distribution, α = .10
913(2)
Table 10 Percentage Points of the F Distribution, α = .05
915(2)
Table 11 Percentage Points of the F Distribution, α = .025
917(2)
Table 12 Percentage Points of the F Distribution, α = .01
919(2)
Table 13 Percentage Points of the Studentized Range q (p, v), α = .05
921(2)
Table 14 Percentage Points of the Studentized Range q (p, v), α = .01
923(2)
Table 15 Critical Values of TL and TU for the Wilcoxon Rank Sum Test: Independent Samples
925(1)
Table 16 Critical Values of T0 for the Wilcoxon Matched-Pairs Signed Rank Test
926(1)
Table 17 Critical Values of Spearman's Rank Correlation Coefficient
927(1)
Table 18 Critical Values of C for the Theil Zero-Slope Test
928(4)
Table 19 Factors Used When Constructing Control Charts
932(1)
Table 20 Values of K for Tolerance Limits for Normal Distributions
933(1)
Table 21 Sample Size n for Nonparametric Tolerance Limits
934(1)
Table 22 Sample Size Code Letters: MIL-STD-105D
934(1)
Table 23 A Portion of the Master Table for Normal Inspection (Single Sampling): MIL-STD-105D
935(1)
Appendix C SAS for Windows Tutorial
936(29)
Appendix D MINITAB for Windows Tutorial
965(33)
Appendix E SPSS for Windows Tutorial
998(30)
References 1028(8)
Selected Short Answers 1036(13)
Credits 1049(5)
Index 1054


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