An Introduction to Probability and Statistical Inference 2nd edition by George Roussas – Ebook PDF Instant Download/Delivery: 0128001143, 978-0128001141
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ISBN 10: 0128001143
ISBN 13: 978-0128001141
Author: George Roussas
An Introduction to Probability and Statistical Inference, Second Edition, guides you through probability models and statistical methods and helps you to think critically about various concepts. Written by award-winning author George Roussas, this book introduces readers with no prior knowledge in probability or statistics to a thinking process to help them obtain the best solution to a posed question or situation. It provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations.
This text contains an enhanced number of exercises and graphical illustrations where appropriate to motivate the reader and demonstrate the applicability of probability and statistical inference in a great variety of human activities. Reorganized material is included in the statistical portion of the book to ensure continuity and enhance understanding. Each section includes relevant proofs where appropriate, followed by exercises with useful clues to their solutions. Furthermore, there are brief answers to even-numbered exercises at the back of the book and detailed solutions to all exercises are available to instructors in an Answers Manual.
An Introduction to Probability and Statistical Inference 2nd Table of contents:
Chapter 1: Some motivating examples and some fundamental concepts
1.1 Some Motivating Examples
1.2 Some Fundamental Concepts
1.3 Random Variables
Chapter 2: The concept of probability and basic results
2.1 Definition of Probability and Some Basic Results
2.2 Distribution of a Random Variable
2.3 Conditional Probability and Related Results
2.4 Independent Events and Related Results
2.5 Basic Concepts and Results in Counting
Chapter 3: Numerical characteristics of a random variable, some special random variables
3.1 Expectation, Variance, and Moment Generating Function of a Random Variable
3.2 Some Probability Inequalities
3.3 Some Special Random Variables
3.4 Median and Mode of a Random Variable
Chapter 4: Joint and conditional p.d.f.’s, conditional expectation and variance, moment generating function, covariance, and correlation coefficient
4.1 Joint D.F. and Joint p.d.f. of Two Random Variables
4.2 Marginal and Conditional p.d.f.’s, Conditional Expectation and Variance
4.3 Expectation of a Function of Two r.v.’s, Joint and Marginal m.g.f.’s, Covariance, and Correlation Coefficient
4.4 Some Generalizations to k Random Variables
4.5 The Multinomial, the Bivariate Normal, and the Multivariate Normal Distributions
Chapter 5: Independence of random variables and some applications
5.1 Independence of Random Variables and Criteria of Independence
5.2 The Reproductive Property of Certain Distributions
Chapter 6: Transformation of random variables
6.1 Transforming a Single Random Variable
6.2 Transforming Two or More Random Variables
6.3 Linear Transformations
6.4 The Probability Integral Transform
6.5 Order Statistics
Chapter 7: Some modes of convergence of random variables, applications
7.1 Convergence in Distribution or in Probability and Their Relationship
7.2 Some Applications of Convergence in Distribution: WLLN and CLT
7.3 Further Limit Theorems
Chapter 8: An overview of statistical inference
8.1 The Basics of Point Estimation
8.2 The Basics of Interval Estimation
8.3 The Basics of Testing Hypotheses
8.4 The Basics of Regression Analysis
8.5 The Basics of Analysis of Variance
8.6 The Basics of Nonparametric Inference
Chapter 9: Point estimation
9.1 Maximum Likelihood Estimation: Motivation and Examples
9.2 Some Properties of MLE’s
9.3 Uniformly Minimum Variance Unbiased Estimates
9.4 Decision-Theoretic Approach to Estimation
9.5 Other Methods of Estimation
Chapter 10: Confidence intervals and confidence regions
10.1 Confidence Intervals
10.2 Confidence Intervals in The Presence of Nuisance Parameters
10.3 A Confidence Region for (μ, σ2) in the N(μ, σ2) Distribution
10.4 Confidence Intervals with Approximate Confidence Coefficient
Chapter 11: Testing hypotheses
11.1 General Concepts, Formulation of Some Testing Hypotheses
11.2 Neyman-Pearson Fundamental Lemma, Exponential Type Families, UMP Tests for Some Composite Hypotheses
11.3 Some Applications of Theorems 2
11.4 Likelihood Ratio Tests
Chapter 12: More about testing hypotheses
12.1 Likelihood Ratio Tests in the Multinomial Case and Contingency Tables
12.2 A Goodness-of-Fit Test
12.3 Decision-Theoretic Approach to Testing Hypotheses
12.4 Relationship between Testing Hypotheses and Confidence Regions
Chapter 13: A simple linear regression model
13.1 Setting up The Model—The Principle of Least Squares
13.2 The Least Squares Estimates of β1 and β2 and Some of Their Properties
13.3 Normally Distributed Errors: MLE’s of β1, β2, and σ2, Some Distributional Results
13.4 Confidence Intervals and Hypotheses Testing Problems
13.5 Some Prediction Problems
13.6 Proof of Theorem 5
13.7 Concluding Remarks
Chapter 14: Two models of analysis of variance
14.1 One-Way Layout with the Same Number of Observations Per Cell
Chapter 15: Some topics in nonparametric inference
15.1 Some Confidence Intervals with Given Approximate Confidence Coefficient
15.2 Confidence Intervals for Quantiles of a Distribution Function
15.3 The Two-Sample Sign Test
15.4 The Rank Sum and the Wilcoxon–Mann–Whitney Two-Sample Tests
15.5 Nonparametric Curve Estimation
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