Description
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For engineering statistics courses in departments of Statistics and Engineering.
This text is designed for a two-semester introductory course in statistics for students majoring in engineering or any of the physical sciences. Inevitalby, once these studenrts 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.The text includes optional theoretical exercises allowing instructors who choose to emphasize theory to do so without requiring additional materials.
The assumed mathematical background is a two-semester sequence in calculus - that is, the course could be taught to students of average mathematical talent and with a basic understanding of the principles of differential and integral calculus.
Datasets and other resources (where applicable) for this book are available here.
New To This Edition
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Although the scope and coverage remain the same, the 5th edition of the text contains several substantial changes, additions, and enhancements:
1.
"Statistics in Action" cases at the end of each chapter.
The text now includes a discussion of an actual, recent scientific study at the end of each chapter.
The data and analysis are discussed in detail, demonstrating how the statistical methods of the chapter are used to answer relevant questions in the field.
2.
Updated statistical software printouts.
Throughout the text we have greatly increased the number of printouts, and now include the newest versions of SPSS and EXCEL output as well as updated SAS and MINITAB printouts.
A printout accompanies every statistical technique presented, allowing the instructor to emphasize interpretations of the statistical results rather than the calculations required to obtain the results.
3.
End-of-chapter summary material.
At the end of each chapter, we provide a summary of the topics presented via a Quick Review (key words and key formulas), Language Lab (a listing of key symbols and pronunciation guide), and Chapter Summary Notes. These features help the student summarize and reinforce the important points from the chapter, and are useful study tools.
4.
More exercises with real data
Many new 'real-life' scientific exercises have been added throughout the text.
All of these are extracted from news articles, scientific magazines, and professional journals.
The inclusion of this real world data allows students to see the connections between the statistics they are learning and how it is used in the real world.
Content Updates & Changes:
5.
Chapter 1:
Statistics and critical thinking.
A new section on the role of statistics in the assessment of the credibility and value of the inferences made from data - i.e., critical thinking (Section 1.4) has been added.
6.
Chapter 2:
Distorting the truth with descriptive statistics.
Several examples on how graphs and numerical descriptive measures can be used to give a distorted view of the data, and how to recognize when this occurs, are now included in a new section (Section 2.8).
7.
Chapters 4-6:
Standard mathematical notation for a random variable.
Throughout the chapters on random variables, we now use the standard mathematical notation for representing a random variable.
Upper case letters are used to represent the random variable, while lower case letters represent the values that the random variable can assume.
8.
Chapter 6:
Monte Carlo simulation.
We emphasize the use of Monte Carlo simulation for approximating a sampling distribution through examples in Section 6.8.
9.
Chapters 7 and 8:
Bootstrapping and Bayesian methods.
New optional sections now present two alternative estimation methods (Section 7.12) and hypothesis testing methods (Section 8.13) - bootstrapping and Bayesian methods.
10.
Chapter 9:
Exact tests for a contingency table.
A new optional section (Section 9.6) presents Fisher's exact test for independence in a 2x2 contingency table.
11.
Chapter 11:
Multiple regression models.
Examples of several different multiple regression models are given in separate sections in Chapter 11 - a 1st-order model (Section 11.7), an interaction model (Section 11.8), and a 2nd-order model (Section 11.9).
12.
Chapter 12:
External model validation.
A new optional section (Section 12.9) on validating a multiple regression model externally has been added to the model building chapter, Chapter 12.
Cross-validation and jackknifing methods are discussed.
13.
Chapters 13 and 14:
Experimental design and ANOVA.
The previous material on analysis of variance is now presented in two chapters:
Principles of Experimental Design (Chapter 13) and ANOVA for Designed Experiments (Chapter 14).
14.
Chapter 16: Capability analysis.
The chapter on statistical process and quality control now includes a new optional section (Section 16.9) on the importance of a capability analysis.
Numerous, less obvious changes in details have been made throughout the text in response to suggestions by current users and reviewers of the text.
Features and Benefits
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Blend of theory and applications.
The basic theoretical concepts of mathematical statistics are integrated with a two-semester presentation of statistical methodology.
Thus, the instructor has the option of presenting a course with either of two characteristics - a course stressing basic concepts and applied statistics or a course that, while still tilted toward application, presents a modes introduction to the theory underlying statistical inference.
Statistical software applications with tutorials.
The instructor and student have the option of using statistical software to perform the statistical calculations.
Printouts from three popular statistical software packages -- SAS, SPSS, and Minitab - as well as Microsoft Excel output are fully integrated into text.
Tutorials with menu screens and dialog boxes are provided in Appendices C, D, and E.
These tutorials are designed for the novice user; no prior experience with the software is needed.
Blended coverage of topics and applications.
To meet the diverse needs of future engineers and scientists, the text provides coverage of a wide range of data analysis topics.
The material on multiple regression and model building (Chapters 11-12), principles of experimental design (Chapter 13), quality control (Chapter 16), and reliability (Chapter 17) sets the text apart from the typical introductory statistics text.
Although the material often refers to theoretical concepts, the presentation is oriented toward applications.
Numerous real data-based exercises.
The text contains a large number of applied exercises designed to motivate a student and suggest future uses of the methodology. Nearly every exercise is based on data or experimental results extracted from professional journals or obtained from organizations in the engineering and physical sciences. Exercises are located at the ends of key sections and at the ends of chapters.
"Statistics in Action" case studies.
The 5th edition of the text now includes a contemporary scientific study ("Statistics in Action") and the accompanying data and analysis at the end of each chapter.
Our goal is to show students the importance of applying sound statistical techniques in order to evaluate the findings and to think through the statistical issues involved.
Data sets provided online.
All of the data sets associated with examples, exercises, and cases are provided online at www.prenhall.com/statistics (search for this title). (Each data set is marked with a CD icon and file name in the text.)
The data files are saved in four different formats: MINITAB, SAS, SPSS, and ASCII (for easy importing into other statistical software packages).
By analyzing these data using statistical software, calculations are minimized, allowing the student to concentrate on the interpretation of the results.
Numerical answers to exercises available at the back of the book.
This gives students the guidence and immediate feedback they require as they work the exercises.
Table of Contents
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CHAPTER 1: INTRODUCTION
1.1
Statistics: The Science of Data
1.2
Fundamental Elements of Statistics
1.3
Types of Data
1.4
The Role of Statistics in Critical Thinking
1.5
A Guide to Statistical Methods Presented in this Text
Statistics in Action: Contamination of Fish in the Tennessee River Collecting theData
CHAPTER 2:
DESCRIPTIVE STATISTICS
2.1
Graphical and Numerical Methods for Describing Qualitative Data
2.2
Graphical Methods for Describing Quantitative Data
2.3
Numerical Methods for Describing Quantitative Data
2.4
Measures of Central Tendency
2.5
Measures of Variation
2.6
Measures of Relative Standing
2.7
Methods for Detecting Outliers
2.8
Distorting the Truth with Descriptive Statistics
Statistics in Action: Characteristics of Contaminated Fish in the Tennessee River
CHAPTER 3:
PROBABILITY
3.1
The Role of Probability in Statistics
3.2
Events, Sample Spaces, and Probability
3.3
Compound Events
3.4
Complementary Events
3.5
Conditional Probability
3.6
Probability Rules for
Unions and Intersections
3.7
Bayes' Rule (Optional)
3.8
Some Counting Rules
3.9
Probability and Statistics: An Example
3.10
Random Sampling
Statistics in Action: Assessing Predictors of Software Defects
CHAPTER 4:
DISCRETE RANDOM VARIABLES
4.1
Discrete Random Variables
4.2
The Probability Distribution for a Discrete Random Variable
4.3
Expected Values for Random Variables
4.4
Some Useful Expectation Theorems
4.5
Bernoulli Trials
4.6
The Binomial Probability Distribution
4.7
The Multinomial Probability Distribution
4.8
The Negative Binomial and the Geometric Probability Distributions
4.9
The Hypergeometric Probability Distribution
4.10
The Poisson Probability Distribution
4.11
Moments and Moment Generating Functions (Optional)
Statistics in Action: The Reliability of a 'One-Shot' Device
CHAPTER 5: CONTINUOUS RANDOM VARIABLES
5.1
Continuous Random Variables
5.2
The Density Function for a Continuous Random Variable
5.3
Expected Values for Continuous Random Variables
5.4
The Uniform Probability Distribution
5.5
The Normal Probability Distribution
5.6
Descriptive Methods for Assessing Normality
5.7
Gamma-Type Probability Distributions
5.8
The Weibull Probability Distriibution
5.9
Beta-Type Probability Distributions
5.10
Moments and Moment Generating Functions (Optional)
Statistics in Action: Super Weapons Development: Optimizing the Hit Ratio
CHAPTER 6: JOINT PROBABILITY DISTRIBUTIONS AND SAMPLING DISTRIBUTIONS
6.1
Bivariate Probability Distributions for Discrete Random Variables
6.2
Bivariate Probability Distributions for Continuous Random Variables
6.3
The Expected Value of Functions of Two Random Variables
6.4
Independence
6.5
The Covariance and Correlation of Two Random Variables
6.6
Probability Distributions and Expected Values of Functions of Random Variables (Optional)
6.7
Sampling Distributions
6.8
Approximating a Sampling Distribution by Monte Carlo Simulation
6.9
The Sampling Distributions of Means and Sums
6.10
Normal Approximation to the Binomial Distribution
6.11
Sampling Distributions Related to the Normal Distribution
Statistics in Action: Availability of an Up/Down System
CHAPTER 7: ESTIMATION USING CONFIDENCE INTERVALS
7.1
Point Estimators and their Properties
7.2
Finding Point Estimators: Classical Methods of Estimation
7.3
Finding Interval Estimators: The Pivotal Method
7.4
Estimation of Population Mean
7.5
Estimation of the Difference Between Two Population Means: Independent Samples
7.6
Estimation of the Difference Between Two Population Means: Matched Pairs
7.7
Estimation of a Poulation Proportion
7.8
Estimation of the Difference Between Two Population Proportions
7.9
Estimation of a Population Variance
7.10
Estimation of the Ratio of Two Population Variances
7.11
Choosing the Sample Size
7.12
Alternative Estimation Methods: Bootstrapping and Bayesian Methods (Optional)
Statistics in Action: Bursting Strength of PET Beverage Bottles
CHAPTER 8:
TESTS OF HYPOTHESES
8.1
The Relationship Between Statistical Tests of Hypotheses and Confidence Intervals
8.2
Elements and Properties of a Statistical Test
8.3
Finding Statistical Tests: Classical Methods
8.4
Choosing the Null and Alternative Hypotheses
8.5
Testing a Population Mean
8.6
The Observed Significance Level for a Test
8.7
Testing the Difference Between Two Population Means: Independent Samples
8.8
Testing the Difference Between Two Population Means: Independent Samples
8.9
Testing a Population Proportion
8.10
Testing the Difference Between Two Population Proportions
8.11
Testing a Population Variance
8.12
Testing the Ration of Two Population Variances
8.13
Alternative Testing Procedures: Bootstrapping and Bayesian Methods (Optional)
Statistics in Action: Comparing Methods for Dissolving Drug Tablets - Dissolution Method Equivalence Testing
CHAPTER 9: CATEGORICAL DATA ANALYSIS
9.1
Categorical Data and Multinomial Probabilities
9.2
Estimating Category Probabilities in a One-Way Table
9.3
Testing Category Probabilities in a One-Way Table
9.4
Inferences About Category Probabilities in a Two-Way (Contingency) Table
9.5
Contingency Tables with Fixed Marginal Totals
9.6
Exact Tests for Independence in a Contingency Table Analysis (Optional)
Statistics in Action: The Public's Perception of Engineers and Engineering
CHAPTER 10: SIMPLE LINEAR REGRESSION
10.1
Regression Models
10.2
Model Assumptions
10.3
Estimating ß0 and ß1: The Method of Least Squares
10.4
Properties of the Least Squares Estimators
10.5
An Estimator of d2
10.6
Assessing the Utility of the Model: Making Inferences About the Slope ß1
10.7
The Coefficient of Correlation
10.8
The Coefficient of Determination
10.9
Using the Model for Estimation and Pediction
10.10 A Complete Example
10.11 A Summary of the Steps to Follow in Simple Linear Regression
Statistics in Action: Can Dowser's Really Detect Water?
CHAPTER 11: MULTIPLE REGRESSION ANALYSIS
11.1
General Form of a Multiple Regression Model
11.2
Model Assumptions
11.3
Fitting the Model:
The Method of Least Squares
11.4
Computations using Matrix Algebra; Estimating and Making Inferences about the ß Parameters
11.5
Assessing Overall Model Adequacy
11.6
A Confidence Interval for E(y) and a prediction interval for a Future Value of y
11.7
A First-Order Model with Quantitative Predictors
11.8
An Interaction Model with Quantitative Predictors
11.9
A Quadratic (Second-Order) Model with a Quantitative Predictor
11.10 Checking Assumptions: Residual Analysis
11.11 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation
11.12 A Summary of the Steps to Follow in a Multiple Regression Analysis
Statistics in Action: Bid-Rigging in the Highway Construction Industry
CHAPTER 12: MODEL BUILDING
12.1
Introduction: Why Model Building is Important
12.2
The Two Types of Independent Variables: Quantitative and Qualitative
12.3
Models with a Single Quantitative Independent Variable
12.4
Models with Two Quantitative Independent Variables
12.5
Coding Quantitative Independent Variables (Optional)
12.6
Models with One Qualitative Independent Variable
12.7
Models with Both Quantitative and Qualitative Independent Variables
12.8
Tests for Comparing Nested Models
12.9
External Model Validation (Optional)
12.10 Stepwise Regression
Statistics in Action: Deregulation of the Intrastate Trucking Industry
CHAPTER 13: PRINCIPLES OF EXPERIMENTAL DESIGN
13.1
Introduction
13.2
Experimental Design Terminology
13.3
Controlling the Information in an Experiment
13.4
Noise-Reducing Designs
13.5
Volume-Increasing Designs
13.6
Selecting the Sample Size
13.7
The Importance of Randomization
Statistics in Action: Anti-Corrosive Behavior of Epoxy Coatings Augmented with Zinc
CHAPTER 14: ANALYSIS OF VARIANCE FOR DESIGNED EXPERIMENTS
14.1
Introduction
14.2
The Logic Behind an Analysis of Variance
14.3
One-Factor Completely Randomized Designs
14.4
Randomized Block Designs
14.5
Two-Factor Factorial Experiments
14.6
More Complex Factorial Designs (Optional)
14.7
Nested Sampling Designs (Optional)
14.8
Multiple Comparisons of Teatment Means
14.9
Checking ANOVA Assumptions
Statistics in Action:
On the Trail of the Cockroach
CHAPTER 15: NONPARAMETRIC STATISTICS
15.1
Introduction: Distribution-Free Tests
15.2
Testing for Location of a Single Population
15.3
Comparing Two Populations: Independent Random Samples
15.4
Comparing Two Populations:
Matched-Pair Design
15.5
Comparing Three or More Populations: Completely Randomized Design
15.6
Comparing Three or More Populations: Randomized Block Design
15.7
Nonparametric Regression
Statistics in Action:
Agent Orange and Vietnam Vets
CHAPTER 16: STATISTICAL PROCESS AND QUALITY CONTROL
16.1
Total Quality Management
16.2
Variable Control Charts
16.3
Control Chart for Means: x-Chart
16.4
Control Chart for Process Variation: R-Chart
16.5
Detecting Trends in a Control Chart: Runs Analysis
16.6
Control Chart for Percent Defective: p-Chart
16.7
Control Chart for number of Defectives per item: c-Chart
16.8
Tolerance Limits
16.9
Capability Analysis (Optional)
16.10
Acceptance Sampling for Defectives
16.11
Other Sampling Plans (Optional)
16.12
Evolutionary Operations (Optional)
Statistics in Action: Testing Jet Fuel Additive for Safety
CHAPTER 17: PRODUCT AND SYSTEM RELIABILITY
17.1
Introduction
17.2
Failure Time Distributions
17.3
Hazard Rates
17.4
Life Testing: Censored Sampling
17.5
Estimating the Parameters of an Exponential Failure Time Distribution
17.6
Estimating the Parameters of a Weibull Failure Time Distribution
17.7
System Reliability
Statistics in Action: Modeling the Hazard Rate of Reinforced Concrete Bridge Deck Deterioration
APPENDIX A: MATRIX ALGEBRA
APPENDIX B: USEFUL STATISTICAL TABLES
APPENDIX C: SAS FOR WINDOWS TUTORIAL
APPENDIX D: MINITAB FOR WINDOWS TUTORIAL
APPENDIX E: SPSS FOR WINDOWS TUTORIAL
ANSWERS TO SELECTED EXERCISES
INDEX
