Introduction to probability and statistics for engineers and scientists /

Introduction to probability and statistics for engineers and scientists /

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Bibliographic Details
Author / Creator:Ross, Sheldon M.
Edition:2nd ed.
Imprint:San Diego, CA : Academic Press, ©2000.
Description:578 pages : illustrations ; 24 cm + 1 computer optical disc (4 3/4 in.)
Language:English
Subject:
Probabilities.
Mathematical statistics.
Mathematical statistics.
Probabilities.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/4040786
Hidden Bibliographic Details
ISBN:0125984723
9780125984720
0125984731
9780125984737
Notes:Includes index.
Table of Contents:
  • Preface
  • Chapter 1. Introduction to Statistics
  • 1.1. Introduction
  • 1.2. Data Collection and Descriptive Statistics
  • 1.3. Inferential Statistics and Probability Models
  • 1.4. Populations and Samples
  • 1.5. A Brief History of Statistics
  • Problems
  • Chapter 2. Descriptive Statistics
  • 2.1. Introduction
  • 2.2. Describing Data Sets
  • 2.2.1. Frequency Tables and Graphs
  • 2.2.2. Relative Frequency Tables and Graphs
  • 2.2.3. Grouped Data, Histograms, Ogives, and Stem and Leaf Plots
  • 2.3. Summarizing Data Sets
  • 2.3.1. Sample Mean, Sample Median, and Sample Mode
  • 2.3.2. Sample Variance and Sample Standard Deviation
  • 2.3.3. Sample Percentiles and Box Plots
  • 2.4. Chebyshev's Inequality
  • 2.5. Normal Data Sets
  • 2.6. Paired Data Sets and the Sample Correlation Coefficient
  • Problems
  • Chapter 3. Elements of Probability
  • 3.1. Introduction
  • 3.2. Sample Space and Events
  • 3.3. Venn Diagrams and the Algebra of Events
  • 3.4. Axioms of Probability
  • 3.5. Sample Spaces Having Equally Likely Outcomes
  • 3.6. Conditional Probability
  • 3.7. Bayes' Formula
  • 3.8. Independent Events
  • Problems
  • Chapter 4. Random Variables and Expectation
  • 4.1. Random Variables
  • 4.2. Types of Random Variables
  • 4.3. Jointly Distributed Random Variables
  • 4.3.1. Independent Random Variables
  • 4.3.2. Conditional Distributions
  • 4.4. Expectation
  • 4.5. Properties of the Expected Value
  • 4.5.1. Expected Value of Sums of Random Variables
  • 4.6. Variance
  • 4.7. Covariance and Variance of Sums of Random Variables
  • 4.8. Moment Generating Functions
  • 4.9. Chebyshev's Inequality and the Weak Law of Large Numbers
  • Problems
  • Chapter 5. Special Random Variables
  • 5.1. The Bernoulli and Binomial Random Variables
  • 5.1.1. Computing the Binomial Distribution Function
  • 5.2. The Poisson Random Variable
  • 5.2.1. Computing the Poisson Distribution Function
  • 5.3. The Hypergeometric Random Variable
  • 5.4. The Uniform Random Variable
  • 5.5. Normal Random Variables
  • 5.6. Exponential Random Variables
  • 5.6.1. The Poisson Process
  • 5.7. The Gamma Distribution
  • 5.8. Distributions Arising from the Normal
  • 5.8.1. The Chi-Square Distribution
  • 5.8.1.1. The Relation between Chi-Square and Gamma Random Variables
  • 5.8.2. The t-Distribution
  • 5.8.3. The F-Distribution
  • Problems
  • Chapter 6. Distributions of Sampling Statistics
  • 6.1. Introduction
  • 6.2. The Sample Mean
  • 6.3. The Central Limit Theorem
  • 6.3.1. Approximate Distribution of the Sample Mean
  • 6.3.2. How Large a Sample Is Needed
  • 6.4. The Sample Variance
  • 6.5. Sampling Distributions from a Normal Population
  • 6.5.1. Distribution of the Sample Mean
  • 6.5.2. Joint Distribution of X and S[superscript 2]
  • 6.6. Sampling from A Finite Population
  • Problems
  • Chapter 7. Parameter Estimation
  • 7.1. Introduction
  • 7.2. Maximum Likelihood Estimators
  • 7.3. Interval Estimates
  • 7.3.1. Confidence Interval for a Normal Mean When the Variance Is Unknown
  • 7.3.2. Confidence Intervals for the Variance of a Normal Distribution
  • 7.4. Estimating the Difference in Means of Two Normal Populations
  • 7.5. Approximate Confidence Interval for the Mean of a Bernoulli Random Variable
  • 7.6. Confidence Interval of the Mean of the Exponential Distribution
  • 7.7. Evaluating a Point Estimator
  • 7.8. The Bayes Estimator
  • Problems
  • Chapter 8. Hypothesis Testing
  • 8.1. Introduction
  • 8.2. Significance Levels
  • 8.3. Tests Concerning the Mean of a Normal Population
  • 8.3.1. Case of Known Variance
  • 8.3.1.1. One-Sided Tests
  • 8.3.2. Case of Unknown Variance: The t-Test
  • 8.4. Testing the Equality of Means of Two Normal Populations
  • 8.4.1. Case of Known Variances
  • 8.4.2. Case of Unknown Variances
  • 8.4.3. Case of Unknown and Unequal Variances
  • 8.4.4. The Paired t-Test
  • 8.5. Hypothesis Tests Concerning the Variance of a Normal Population
  • 8.5.1. Testing for the Equality of Variances of Two Normal Populations
  • 8.6. Hypothesis Tests in Bernoulli Populations
  • 8.6.1. Testing the Equality of Parameters in Two Bernoulli Populations
  • 8.6.1.1. Computations for the Fisher-Irwin Test
  • 8.7. Tests Concerning the Mean of a Poisson Distribution
  • 8.7.1. Testing the Relationship between Two Poisson Parameters
  • Problems
  • Chapter 9. Regression
  • 9.1. Introduction
  • 9.2. Least Squares Estimators of the Regression Parameters
  • 9.3. Distribution of the Estimators
  • 9.4. Statistical Inferences about the Regression Parameters
  • 9.4.1. Inferences Concerning [beta]
  • 9.4.1.1. Regression to the Mean
  • 9.4.2. Inferences Concerning [alpha]
  • 9.4.3. Inferences Concerning the Mean Response [alpha] + [beta]X
  • 9.4.4. Prediction Interval of a Future Response
  • 9.4.5. Summary of Distributional Results
  • 9.5. The Coefficient of Determination and the Sample Correlation Coefficient
  • 9.6. Analysis of Residuals: Assessing the Model
  • 9.7. Transforming to Linearity
  • 9.8. Weighted Least Squares
  • 9.9. Polynomial Regression
  • 9.10. Multiple Linear Regression
  • 9.10.1. Predicting Future Responses
  • Problems
  • Chapter 10. Analysis of Variance
  • 10.1. Introduction
  • 10.2. An Overview
  • 10.3. One-Way Analysis of Variance
  • 10.3.1. Multiple Comparisons of Sample Means
  • 10.3.2. One-Way Analysis of Variance with Unequal Sample Sizes
  • 10.4. Two-Factor Analysis of Variance: Introduction and Parameter Estimation
  • 10.5. Two-Factor Analysis of Variance: Testing Hypotheses
  • 10.6. Two-Way Analysis of Variance with Interaction
  • Problems
  • Chapter 11. Goodness of Fit Tests and Categorical Data Analysis
  • 11.1. Introduction
  • 11.2. Goodness of Fit Tests When All Parameters Are Specified
  • 11.2.1. Determining the Critical Region by Simulation
  • 11.3. Goodness of Fit Tests When Some Parameters Are Unspecified
  • 11.4. Tests of Independence in Contingency Tables
  • 11.5. Tests of Independence in Contingency Tables Having Fixed Marginal Totals
  • 11.6. The Kolmogorov-Smirnov Goodness of Fit Test for Continuous Data
  • Problems
  • Chapter 12. Nonparametric Hypothesis Tests
  • 12.1. Introduction
  • 12.2. The Sign Test
  • 12.3. The Signed Rank Test
  • 12.4. The Two-Sample Problem
  • 12.4.1. The Classical Approximation and Simulation
  • 12.5. The Runs Test for Randomness
  • Problems
  • Chapter 13. Quality Control
  • 13.1. Introduction
  • 13.2. Control Charts for Average Values: The X-Control Charts
  • 13.2.1. Case of Unknown [mu] and [sigma]
  • 13.3. S-Control Charts
  • 13.4. Control Charts for the Fraction Defective
  • 13.5. Control Charts for Number of Defects
  • 13.6. Other Control Charts for Detecting Changes in the Population Mean
  • 13.6.1. Moving-Average Control Charts
  • 13.6.2. Exponentially Weighted Moving-Average Control Charts
  • 13.6.3. Cumulative Sum Control Charts
  • Problems
  • Chapter 14. Life Testing
  • 14.1. Introduction
  • 14.2. Hazard Rate Functions
  • 14.3. The Exponential Distribution in Life Testing
  • 14.3.1. Simultaneous Testing -- Stopping at the rth Failure
  • 14.3.2. Sequential Testing
  • 14.3.3. Simultaneous Testing -- Stopping by a Fixed Time
  • 14.3.4. The Bayesian Approach
  • 14.4. A Two-Sample Problem
  • 14.5. The Weibull Distribution in Life Testing
  • 14.5.1. Parameter Estimation by Least Squares
  • Problems
  • Appendix of Tables
  • Index
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