This well-respected text is designed for the first course in probability and statistics taken by students majoring
in Engineering and the Computing Sciences. The prerequisite is one year of calculus. The text offers a balanced
presentation of applications and theory. The authors take care to develop the theoretical foundations for the statistical
methods presented at a level that is accessible to students with only a calculus background. They explore the practical
implications of the formal results to problem-solving so students gain an understanding of the logic behind the
techniques as well as practice in using them. The examples, exercises, and applications were chosen specifically
for students in engineering and computer science and include opportunities for real data analysis.
New to This Edition:
References to statistical analysis packages have been added. This enables students to see how the techniques
are applied by using these popular packages.
Some of the more difficult derivations have been moved to an appendix.
Discussion of Tukey's method of paired comparisons and a section on the use of tolerance limits in quality
control
Features:
New and retained examples and exercises are chosen specifically for students in engineering and computer science.
Students can work on problems within a context that is familiar and interesting to them.
Students gain understanding of the logic behind the techniques as well as practice using them due to the authors'
approach. Because of this, students are better able to retain the information and are better prepared to apply
the techniques in this and future courses.
Data sets for examples and problems are provided in electronic format for use with the most popular statistical
packages. Students do not have to waste valuable time entering data and data-entry errors are eliminated.
Student Solutions Manual and an Instructor's Manual are available.
Table of Contents
1. Introduction to Probability and Counting
1.1 Interpreting Probabilities
1.2 Sample Spaces and Events
1.3 Permutations and Combinations
2. Some Probability Laws
2.1 Axioms of Probability
2.2 Conditional Probability
2.3 Independence and the Multiplication Rule
2.4 Bayes' Theorem
3. Discrete Distributions
3.1 Random Variables
3.2 Discrete Probablility Densities
3.3 Expectation and Distribution Parameters
3.4 Geometric Distribution and the Moment Generating Function
3.5 Binomial Distribution
3.6 Negative Binomial Distribution
3.7 Hypergeometric Distribution
3.8 Poisson Distribution
4. Continuous Distributions
4.1 Continuous Densities
4.2 Expectation and Distribution Parameters
4.3 Gamma Distribution
4.4 Normal Distribution
4.5 Normal Probability Rule and Chebyshev's Inequality
4.6 Normal Approximation to the Binomial Distribution
4.7 Weibull Distribution and Reliability
4.8 Transformation of Variables
4.9 Simulating a Continuous Distribution
5. Joint Distributions
5.1 Joint Densities and Independence
5.2 Expectation and Covariance
5.3 Correlation
5.4 Conditional Densities and Regression
5.5 Transformation of Variables
6. Descriptive Statistics
6.1 Random Sampling
6.2 Picturing the Distribution
6.3 Sample Statistics
6.4 Boxplots
7. Estimation
7.1 Point Estimation
7.2 The Method of Moments and Maximum Likelihood
7.3 Functions of Random Variables--Distribution of X
7.4 Interval Estimation and the Central Limit Theorem
8. Inferences on the Mean and Variance of a Distribution
8.1 Interval Estimation of Variability
8.2 Estimating the Mean and the Student-t Distribution
8.3 Hypothesis Testing
8.4 Significance Testing
8.5 Hypothesis and Significance Tests on the Mean
8.6 Hypothesis Tests
8.7 Alternative Nonparametric Methods
9. Inferences on Proportions
9.1 Estimating Proportions
9.2 Testing Hypothesis on a Proportion
9.3 Comparing Two Proportions: Estimation
9.4 Coparing Two Proportions: Hypothesis Testing
10.Comparing Two Means and Two Variances
10.1 Point Estimation
10.2 Comparing Variances: The F Distribution
10.3 Comparing Means: Variances Equal (Pooled Test)
10.4 Comparing Means: Variances Unequal
10.5 Compairing Means: Paried Data
10.6 Alternative Nonparametric Methods
10.7 A Note on Technology
11. Sample Linear Regression and Correlation
11.1 Model and Parameter Estimation
11.2 Properties of Least-Squares Estimators
11.3 Confidence Interval Estimation and Hypothesis Testing
11.4 Repeated Measurements and Lack of Fit
11.5 Residual Analysis
11.6 Correlation
12. Multiple Linear Regression Models
12.1 Least-Squares Procedures for Model Fitting
12.2 A Matrix Approach to Least Squares
12.3 Properties of the Least-Squares Estimators
12.4 Interval Estimation
12.5 Testing Hypotheses about Model Parameters
12.6 Use of Indicator or "Dummy" Variables
12.7 Criteria for Variable Selection
12.8 Model Transformation and Concluding Remarks
13. Analysis of Variance
13.1 One-Way Classification Fixed-Effects Model
13.2 Comparing Variances
13.3 Pairwise Comparison
13.4 Testing Contrasts
13.5 Randomized Complete Block Design
13.6 Latin Squares
13.7 Random-Effects Models
13.8 Design Models in Matrix Form
13.9 Alternative Nonparametric Methods
14. Factorial Experiments
14.1 Two-Factor Analysis of Variance
14.2 Extension to Three Factors
14.3 Random and Mixed Model Factorial Experiments
14.4 2^k Factorial Experiments
14.5 2^k Factorial Experiments in an Incomplete Block Design
14.6 Fractional Factorial Experiments
15. Categorical Data
15.1 Multinomial Distribution
15.2 Chi-Squared Goodness of Fit Tests
15.3 Testing for Independence
15.4 Comparing Proportions
16. Statistical Quality Control
16.1 Properties of Control Charts
16.2 Shewart Control Charts for Measurements
16.3 Shewart Control Charts for Attributes
16.4 Tolerance Limits
16.5 Acceptance Sampling
16.6 Two-Stage Acceptance Sampling
16.7 Extensions in Quality Control
Appendix A Statistical Tables
Appendix B Answers to Selected Problems
Appendix C Selected Derivations
