Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences (Int'l Ed)

Chapter 1 - Introduction to Probability and Counting

1.1 Interpreting Probabilities

1.2 Sample Spaces and Events

1.3 Permutations and Combinations

Chapter Summary

Exercises

Review Exercises

Chapter 2 - Some Probability Laws

2.1 Axioms of Probability

2.2 Conditional Probability

2.3 Independence and the Multiplication Rule

2.4 Bayes' Theorem

Chapter Summary

Exercises

Review Exercises

Chapter 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

Chapter Summary

Exercises

Review Exercises

Chapter 4 - Continuous Distributions

4.1 Continuous Densities

4.2 Expectation and Distribution Parameters

4.3 Gamma, Exponential, and Chi-Squared Distributions

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

Chapter Summary

Exercises

Review Exercises

Chapter 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

Chapter Summary

Exercises

Review Exercises

Chapter 6 - Descriptive Statistics

6.1 Random Sampling

6.2 Picturing the Distribution

6.3 Sample Statistics

6.4 Boxplots

Chapter Summary

Exercises

Review Exercises

Chapter 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

Chapter Summary

Exercises

Review Exercises

Chapter 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 Test on the Variance

8.7 Alternative Nonparametric Methods

Chapter Summary

Exercises

Review Exercises

Chapter 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

Chapter Summary

Exercises

Review Exercises

Chapter 10 - Comparing Two Means and Two Variances

10.1 Point Estimation: Independent Samples

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

Chapter Summary

Exercises

Review Exercises

Chapter 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

Chapter Summary

Exercises

Review Exercises

Chapter 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 Hypothesis about Model Parameters

12.6 Use of Indicator or

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.