Applied Probability and Statistics 2006th Edition by Mario Lefebvre ISBN 0387284540 9780387284545

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ISBN 10:       0387284540
ISBN 13: 978-0387284545
Author:         Mario Lefebvre

Applied Probability and Statistics 2006th Table of contents:

Chapter 1: Introduction to Probability

• 1.1 Basic Concepts of Probability
• 1.2 Sample Spaces and Events
• 1.3 Conditional Probability
• 1.4 Bayes’ Theorem
• 1.5 Random Variables and Probability Distributions

Chapter 2: Discrete Probability Distributions

• 2.1 Discrete Random Variables
• 2.2 Probability Mass Function (PMF)
• 2.3 Binomial Distribution
• 2.4 Poisson Distribution
• 2.5 Hypergeometric Distribution
• 2.6 Other Discrete Distributions

Chapter 3: Continuous Probability Distributions

• 3.1 Continuous Random Variables
• 3.2 Probability Density Function (PDF)
• 3.3 Uniform Distribution
• 3.4 Normal Distribution
• 3.5 Exponential Distribution
• 3.6 Gamma Distribution

Chapter 4: Joint Probability Distributions

• 4.1 Joint Distributions for Two Variables
• 4.2 Conditional Distributions
• 4.3 Covariance and Correlation
• 4.4 Independence of Random Variables

Chapter 5: Point Estimation and Sampling Distributions

• 5.1 Point Estimators and Properties
• 5.2 Sampling Distributions
• 5.3 Central Limit Theorem
• 5.4 Estimation of Means, Variances, and Proportions
• 5.5 Methods of Moments and Maximum Likelihood

Chapter 6: Interval Estimation

• 6.1 Confidence Intervals
• 6.2 Confidence Intervals for the Mean
• 6.3 Confidence Intervals for Proportions
• 6.4 Sample Size Determination for Estimation

Chapter 7: Hypothesis Testing

• 7.1 Introduction to Hypothesis Testing
• 7.2 Types of Errors: Type I and Type II
• 7.3 One-Sample Hypothesis Tests
• 7.4 Two-Sample Hypothesis Tests
• 7.5 Hypothesis Testing for Proportions
• 7.6 Goodness-of-Fit Tests

Chapter 8: Analysis of Variance (ANOVA)

• 8.1 One-Way ANOVA
• 8.2 Two-Way ANOVA
• 8.3 Assumptions and Diagnostics
• 8.4 Post-Hoc Tests

Chapter 9: Linear Regression and Correlation

• 9.1 Simple Linear Regression
• 9.2 Least Squares Estimation
• 9.3 Multiple Linear Regression
• 9.4 Correlation Analysis
• 9.5 Regression Diagnostics and Model Evaluation

Chapter 10: Nonparametric Methods

• 10.1 Introduction to Nonparametric Methods
• 10.2 Chi-Square Tests
• 10.3 Rank-Sum Tests
• 10.4 Kruskal-Wallis Test
• 10.5 Wilcoxon Signed-Rank Test

Chapter 11: Time Series Analysis

• 11.1 Components of Time Series Data
• 11.2 Moving Averages
• 11.3 Autocorrelation and Partial Autocorrelation
• 11.4 Forecasting Models
• 11.5 ARIMA Models

Chapter 12: Statistical Quality Control

• 12.1 Introduction to Quality Control
• 12.2 Control Charts for Variables
• 12.3 Control Charts for Attributes
• 12.4 Process Capability Analysis
• 12.5 Acceptance Sampling

Chapter 13: Multivariate Analysis

• 13.1 Introduction to Multivariate Data
• 13.2 Principal Component Analysis
• 13.3 Factor Analysis
• 13.4 Cluster Analysis
• 13.5 Discriminant Analysis

Chapter 14: Simulation and Resampling Methods

• 14.1 Introduction to Monte Carlo Simulation
• 14.2 Resampling Methods: Bootstrap and Jackknife
• 14.3 Markov Chains and Markov Models

Chapter 15: Decision Theory and Statistical Inference

• 15.1 Decision Making under Uncertainty
• 15.2 Decision Trees
• 15.3 Bayesian Inference
• 15.4 Decision Rules and Optimal Decision Making

Chapter 16: Advanced Topics in Probability and Statistics

• 16.1 Stochastic Processes
• 16.2 Markov Chains
• 16.3 Queuing Theory
• 16.4 Reliability Analysis

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