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Probability and Statistics for Engineering and the Sciences, Enhanced Review Edition,9780495557449
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Probability and Statistics for Engineering and the Sciences, Enhanced Review Edition



Pub. Date:
Duxbury Press
Includes 2-weeks free access to
step-by-step solutions for this book.
Step-by-Step solutions are actual worked out problems to the questions at the end of each chapter that help you understand your homework and study for your exams. Chegg and eCampus are providing you two weeks absolutely free. 81% of students said using Step-by-Step solutions prepared them for their exams.
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This comprehensive introduction to probability and statistics will give you the solid grounding you need no matter what your engineering specialty. Through the use of lively and realistic examples, the author helps you go beyond simply learning about statistics to actually putting the statistical methods to use. Rather than focus on rigorous mathematical development and potentially overwhelming derivations, the book emphasizes concepts, models, methodology, and applications that facilitate your understanding.

Table of Contents

Overview and Descriptive Statistics
Populations, Samples, and Processes
Pictorial and Tabular Methods in Descriptive Statistics
Measures of Location
Measures of Variability
Sample Spaces and Events
Axioms, Interpretations, and Properties of Probability
Counting Techniques
Conditional Probability
Discrete Random Variables and Probability Distributions
Random Variables
Probability Distributions for Discrete Random Variables
Expected Values of Discrete Random Variables
The Binomial Probability Distribution
Hypergeometric and Negative Binomial Distributions
The Poisson Probability Distribution
Continuous Random Variables and Probability Distributions
Continuous Random Variables and Probability Density Functions
Cumulative Distribution Functions and Expected Values
The Normal Distribution
The Exponential and Gamma Distribution
Other Continuous Distributions
Probability Plots
Joint Probability Distributions and Random Samples
Jointly Distributed Random Variables
Expected Values, Covariance, and Correlation
Statistics and Their Distributions
The Distribution of the Sample Mean
The Distribution of a Linear Combination
Point Estimation
Some General Concepts of Point Estimation
Methods of Point Estimation
Statistical Intervals Based on a Single Sample
Basic Properties of Confidence Intervals
Large-Sample Confidence Intervals for a Population Mean and Proportion
Intervals Based on a Normal Population Distribution
Confidence Intervals for the Variance and Standard Deviation of a Normal Population
Tests of Hypotheses Based on a Single Sample
Hypothesis and Test Procedures
Tests About a Population Mean
Tests Concerning a Population Proportion
Some Comments on Selecting a Test
Inferences Based on two Samples
z Tests and Confidence Intervals for a Difference Between Two Population Means
The Two-Sample t Test and Confidence Interval
Analysis of Paired Data
Inferences Concerning a Difference Between Population Proportions
Inferences Concerning Two Population Variances
The Analysis of Variance
Single-Factor ANOVA
Multiple Comparisons in ANOVA
More on Single-Factor ANOVA
Multifactor Analysis of Variance
Two-Factor ANOVA with Kij =
Two-Factor ANOVA with Kij >
Three-Factor ANOVA. 2p Factorial Experiments
Simple Linear Regression and Correlation
The Simple Linear Regression Model
Estimating Model Parameters
Inferences About the Slope Parameter G
Inferences Concerning &Y-x* and the Prediction of Future Y Values
Nonlinear and Multiple Regression
Aptness of the Model and Model Checking
Regression with Transformed Variables
Polynomial Regression
Multiple Regression Analysis
Other Issues in Multiple Regression
Goodness-of-Fit Tests and Categorical Data Analysis
Goodness-of-Fit Tests When Category Probabilities are Completely Specified
Goodness of Fit for Composite Hypotheses
Two-Way Contingency Tables
Distribution-Free Procedures
The Wilcoxon Signed-Rank Test
The Wilcoxon Rank-Sum Test
Distribution-Free Confidence Intervals
Distribution-Free ANOVA
Quality Control Methods
General Comments on Control Charts
Control Charts fort Process Location
Control Charts for Process Variation
Control Charts for Attributes
CUSUM Procedures
Acceptance Sampling
Cumulative Binomial Probabilities
Cumulative Poisson Probabilities
Standard Normal Curve Areas
The Incomplete Gamma Function
Critical Values for t Distributions
Tolerance Critical Values for Normal Population Distributions
Critical Values for Chi-Squared Distributions
t Curve Tail Areas
Critical Values for F Distributions
Critical Values for Studentized Range Distributions
Chi-Squared Curve Tail Areas
Critical Values for the Ryan-Joiner Test of Normality
Critical Values for the Wilcoxon Signed-Rank Test
Critical Values for the Wilcoxon Rank-Sum Test
Critical Value
Table of Contents provided by Publisher. All Rights Reserved.

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