Probability and Statistics for Engineers and Scientists

ISBN-10: 0130415294

ISBN-13: 9780130415295

Edition: 7th 2002

Authors: Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye

List price: $127.60
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This classic book provides a rigorous introduction to basic probability theory and statistical inference that is motivated by interesting, relevant applications. It assumes readers have a background in calculus, and offers a unique balance of theory and methodology.Chapter topics cover an introduction to statistics and data analysis, probability, random variables and probability distributions, mathematical expectation, some discrete probability distributions, some continuous probability distributions, functions of random variables, fundamental sampling distributions and data descriptions, one- and two-sample estimation problems, one- and two-sample tests of hypotheses, simple linear regression and correlation, multiple linear regression and certain nonlinear regression models, one factor experiments: general, factorial experiments (two or more factors), 2 k factorial experiments and fractions, nonparametric statistics, and statistical quality control.For individuals trying to apply statistical concepts to real-life, and analyze and interpret data.
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Book details

List price: $127.60
Edition: 7th
Copyright year: 2002
Publisher: Prentice Hall PTR
Publication date: 10/10/2001
Binding: Hardcover
Pages: 730
Size: 7.75" wide x 9.50" long x 1.25" tall
Weight: 2.838
Language: English

Introduction to Statistics and Data Analysis
Overview: Statistical Inference, Samples, Populations, and Experimental Design
The Role of Probability
Sampling Procedures
Collection of Data
Measures of Location: The Sample Mean
Measures of Variability
Discrete and Continuous Data
Statistical Modeling, Scientific Inspection, and Graphical Diagnostics
Graphical Methods and Data Description
Sample Space
Counting Sample Points
Probability of an Event
Additive Rules
Conditional Probability
Multiplicative Rules
Bayes' Rule
Random Variables and Probability Distributions
Concept of a Random Variable
Discrete Probability Distributions
Continuous Probability Distributions
Joint Probability Distributions
Mathematical Expectation
Mean of a Random Variable
Variance and Covariance
Means and Variances of Linear Combinations of Random Variables
Chebyshev's Theorem
Some Discrete Probability Distributions
Discrete Uniform Distribution
Binomial and Multinomial Distributions
Hypergeometric Distribution
Negative Binomial and Geometric Distributions
Poisson Distribution and the Poisson Process
Some Continuous Probability Distributions
Continuous Uniform Distribution
Normal Distribution
Areas Under the Normal Curve
Applications of the Normal Distribution
Normal Approximation to the Binomial
Gamma and Exponential Distributions
Applications of the Exponential and Gamma Distributions
Chi-Squared Distribution
Lognormal Distribution
Weibull Distribution
Functions of Random Variables (Optional)
Transformations of Variables
Moments and Moment-Generating Functions
Fundamental Sampling Distributions and Data Descriptions
Random Sampling
Some Important Statistics
Data Displays and Graphical Methods
Sampling Distributions
Sampling Distribution of Means
Sampling Distribution of S2
One- and Two-Sample Estimation Problems
Statistical Inference
Classical Methods of Estimation
Single Sample: Estimating the Mean
Standard Error of a Point Estimate
Prediction Interval
Tolerance Limits
Two Samples: Estimating the Difference Between Two Means
Paired Observations
Single Sample: Estimating a Proportion
Two Samples: Estimating the Difference Between Two Proportions
Single Sample: Estimating the Variance
Two Samples: Estimating the Ratio of Two Variances
Bayesian Methods of Estimation (Optional)
Maximum Likelihood Estimation (Optional)
One- and Two-Sample Tests of Hypotheses
Statistical Hypotheses: General Concepts
Testing a Statistical Hypothesis
One- and Two-Tailed Tests/ The Use of P/D-Values for Decision Making
Single Sample: Tests Concerning a Single Mean (Variance Known)
Relationship to Confidence Interval Estimation
Single Sample: Tests on a Single Mean (Variance Unknown)
Two Samples: Tests on Two Means
Choice of Sample Size for Testing Means
Graphical Methods for Comparing Means
One Sample: Test on a Single Proportion
Two Samples: Tests on Two Proportions
One- and Two-Sample Tests Concerning Variances
Goodness-of-Fit Test
Test for Independence (Categorical Data)
Test for Homogeneity
Testing for Several Proportions
Two-Sample Case Study
Simple Linear Regression and Correlation
Introduction to Linear Regression
Simple Linear Regression
Least Squares and The Fitted Model
Properties of the Least Squares Estimators
Inferences Concerning the Regression Coefficients
Choice of a Regression Model
Analysis-of-Variance Approach
Test for Linearity of Regression: Data with Repeated Observations
Data Plots and Transformations
Simple Linear Regression Case Study
Multiple Linear Regression and Certain Nonlinear Regression Models
Estimating the Coefficients
Linear Regression Model Using Matrices (Optional)
Properties of the Least Squares Estimators
Inferences in Multiple Linear Regression
Choice of a Fitted Model Through Hypothesis Testing
Special Case of Orthogonality (Optional)
Categorical or Indicator Variables
Sequential Methods for Model Selection
Study of Residuals and Violation of Assumptions
Cross Validation, Cvp, and Other Criteria for Model Selection
Special Nonlinear Models for Nonideal Conditions
One Factor Experiments: General
Analysis-of-Variance Technique
The Strategy of Experimental Design
One-Way Analysis of Variance: Completely Randomized Design
Tests for the Equality of Several Variances
Single-Degree-of-Freedom Comparisons
Multiple Comparisons
Comparing Treatments with a Control
Comparing a Set of Treatments in Blocks
Randomized Complete Block Designs
Graphical Methods and Further Diagnostics
Latin Squares (Optional)
Random Effects Models
Power of Analysis-of-Variance Tests
Case Study
Factorial Experiments (Two or More Factors)
Interaction and the Two-Factor Experiment
Two-Factor Analysis of Variance
Graphical Analysis in the Two-Factor Problem
Three-Factor Experiments
Model II and III Factorial Experiments
Choice of Sample Size
2k Factorial Experiments and Fractions
Analysis of Variance and the Calculation of Effects
Nonreplicated 2k Factorial Experiment
Injection Molding Case Study
Factorial Experiments in Incomplete Blocks
Partial Confounding
Factorial Experiments in a Regression Setting
The Orthogonal Design
Fractional Factorial Experiments
Analysis of Fractional Factorial Experiments
Higher Fractions and Screening Designs
Construction of Resolution III and IV Designs with 8,16, and 32 Design Points
Other Two-Level Resolution III Designs
The Plackett-Burman Designs
Taguchi's Robust Parameter Design
Nonparametric Statistics
Nonparametric Tests
Sign Test
Signed-Rank Test
Rank-Sum Test
Kruskal-Wallis Test
Tolerance Limits
Rank Correlation Coefficient
Statistical Quality Control
Nature of the Control Limits
Purposes of the Control Chart
Control Charts for Variables
Control Charts for Attributes
Cusum Control Charts
Appendix: Statistical Tables and Proofs of Some Theoretical Results
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