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  SPECIFICATION
INTRODUCTION TO PROBABILITY AND STATISTICS: PRINCIPLES AND APPLICATIONS FOR ENGINEERING AND COMPUTING (pb)
Author: MILTON
ISBN: 007063694X
Year: 2007
Publisher: MCGRAW-HILL EDU (INDIA) PVT LTD.
Category: MATHEMATICS
Edition: 1
Format: Paperback
Language: English
Pages: 798
 ABOUT THE TITLE
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.
Key Features
. References to statistical analysis packages have been added. This enables students to see how the techniques are applied by using these popular packages.
. SSM and IM provided.
. New and retained examples and exercises are chosen specifically for students in engineering and computer science. Students can therefore work on problems within a context that is familiar and interesting to them.
. The presentation is such that students get some understanding of the logic behind the techniques as well as practice using them. Because students will have some understanding of the logic behind the techniques students are better able to retain the information and are better prepared to apply the techniques.
. Data sets for examples and problems are provided in several electronic formats for use with the most popular statistical packages. Students do not have to waste valuable time entering data and data-entry errors are eliminated.
. Do you have lots of Engineering and Computer Science majors? Do you like to use specific examples that relate to their field of study?
. Are you looking for a text that helps to reinforce the logic along with the techniques?
. Do you like to use Data sets? Do you find that problems arise when students have to manually enter the data sets?
. Would you like your students to see references to popular statistical analysis packages?
AUTHOR:
J. Susan Milton AND Jesse Arnold
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 TABLE OF CONTENTS
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 "Dummy" Variables (Optional)
12.7 Criteria for Variable Selection
12.8 Model Transformation and Concluding Remarks
Chapter Summary
Exercises
Review Exercises
Chapter 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 Nonparametirc Methods
Chapter Summary
Exercises
Review Exercises
Chapter 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 2k Factorial Experiments
14.5 2k Factorial Experiments in an Incomplete Block Design
14.6 Fractional Factorial Experiments
Chapter Summary
Exercises
Review Exercises
Chapter 15 - Categorical Data
15.1 Multinomial Distribution
15.2 Chi-Squared Goodness of Fit Tests
15.3 Testing for Independence
15.4 Comparing Proportions
Chapter Summary
Exercises
Review Exercises
Chapter 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
Chapter Summary
Exercises
Review Exercies
Appendix A - Statistical Tables
Appendix B - Answers to Selected Problems
Appendix C - Selected Derivations
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