Probability and Statistics for Engineers and Scientists

ISBN-10: 0495107573

ISBN-13: 9780495107576

Edition: 3rd 2007

List price: $341.95
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The new edition of Anthony Hayter's book continues in the same student-oriented vein that has made previous editions successful. Because Tony Hayter teaches and conducts research at a premier engineering school, he is in touch with engineers daily and understands their vocabulary. This leads to a clear and more readable writing style that students understand and appreciate. Additionally, because of his intimacy with the professional community, Hayter includes many high-interest examples and datasets that keep students' attention throughout the term. PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS employs a flexible approach with regard to the use of computer tools. Because the book is not tied to a particular software package, instructors may choose the program that best suits their needs. However, the book does provide substantial computer output (using MINITAB and other programs) to give students the necessary practice in interpreting output. "Computer Note" sections offer tips for using various software packages to perform analysis of the datasets, which can be downloaded from the website. Through the use of extensive examples and datasets, the book illustrates the importance of statistical data collection and analysis for students in the fields of aerospace, biochemical, civil, electrical, environmental, industrial, mechanical, and textile engineering, as well as for students in physics, chemistry, computing, biology, management, and mathematics.
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Book details

List price: $341.95
Edition: 3rd
Copyright year: 2007
Publisher: Brooks/Cole
Publication date: 2/3/2006
Binding: Hardcover
Pages: 832
Size: 8.25" wide x 10.00" long x 1.00" tall
Weight: 3.520
Language: English

Probability Theory
Combinations of Events
Conditional Probability
Probabilities of Event Intersections
Posterior Probabilities
Counting Techniques
Random Variables
Discrete Random Variables
Continuous Random Variables
The Expectation of a Random Variable
The Variance of a Random Variable
Jointly Distributed Random Variables
Combinations and Functions of Random Variables
Discrete Probability Distributions
The Binomial Distribution
The Geometric and Negative Binomial Distributions
The Hypergeometric Distribution
The Poisson Distribution
The Multinomial Distribution
Continuous Probability Distributions
The Uniform Distribution
The Exponential Distribution
The Gamma Distribution
The Weibull Distribution
The Beta Distribution
The Normal Distribution
Probability Calculations Using the Normal Distribution
Linear Combinations of Normal Random Variables
Approximating Distributions with the Normal Distribution
Distributions Related to the Normal Distribution
Descriptive Statistics
Data Presentation
Sample Statistics
Statistical Estimation and Sampling Distributions
Point Estimates
Properties of Point Estimates
Sampling Distributions
Constructing Parameter Estimates
Inferences on a Population Mean
Confidence Intervals
Hypothesis Testing
Comparing Two Population Means
Analysis of Paired Samples
Analysis of Independent Samples
Discrete Data Analysis
Inferences on a population Proportion
Comparing Two Population Proportions
Goodness-of-Fit Tests for One-Way Contingency Tables
Testing for Independence in Two-Way Contingency Tables
The Analysis of Variance
One Factor Analysis of Variance
Randomized Block Designs
Simple Linear Regression and Correlation
The Simple Linear Regression Model
Fitting the Regression Line
Inferences on the Slope Parameter /
Inferences on the Regression Line
Prediction Intervals for Future Response Values
The Analysis of Variance Tables
Residual Analysis
Variable Transformation
Correlation Analysis
Multiple Linear Regression and Nonlinear Regression
Introduction to Multiple Linear Regression
Examples of Multiple Linear Regression
Matrix Algebra Formulation of Multi-le Linear Regression
Evaluating Model Adequacy
Nonlinear Regression
Multifactor Experimental Design and Analysis
Experiments with Two Factors
Experiments with Three or More Factors
Nonparametric Statistical Analysis
The analysis of a single Population
Comparing Two Populations
Comparing Three or More Populations
Quality Control Methods
Statistical Process control
Variable Control Charts
Attribute Control Charts
Acceptance Sampling
Reliability Analysis and Life Testing
System Reliability
Modeling Failure Rates
Life Testing
Answers to Odd-Numbered Problems
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