Mathematical Statistics and Data Analysis 3rd Edition by John A. Rice – Ebook PDF Instant Download/Delivery: 1111793719, 9781111793715
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ISBN 10: 1111793719
ISBN 13: 9781111793715
Author: John A. Rice
This is the first text in a generation to re-examine the purpose of the mathematical statistics course. The book’s approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book’s descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts that are set in abstract settings.
Mathematical Statistics and Data Analysis 3rd Table of contents:
CHAPTER 1: Probability
1.1: Introduction
1.2: Sample Spaces
1.3: Probability Measures
1.4: Computing Probabilities: Counting Methods
1.5: Conditional Probability
1.6: Independence
1.7: Concluding Remarks
1.8: Problems
CHAPTER 2: Random Variables
2.1: Discrete Random Variables
2.2: Continuous Random Variables
2.3: Functions of a Random Variable
2.4: Concluding Remarks
2.5: Problems
CHAPTER 3: Joint Distributions
3.1: Introduction
3.2: Discrete Random Variables
3.3: Continuous Random Variables
3.4: Independent Random Variables
3.5: Conditional Distributions
3.6: Functions of Jointly Distributed Random Variables
3.7: Extrema and Order Statistics
3.8: Problems
CHAPTER 4: Expected Values
4.1: The Expected Value of a Random Variable
4.2: Variance and Standard Deviation
4.3: Covariance and Correlation
4.4: Conditional Expectation and Prediction
4.5: The Moment-Generating Function
4.6: Approximate Methods
4.7: Problems
CHAPTER 5: Limit Theorems
5.1: Introduction
5.2: The Law of Large Numbers
5.3: Convergence in Distribution and the Central Limit Theorem
5.4: Problems
CHAPTER 6: Distributions Derived from the Normal Distribution
6.1: Introduction
6.2: χ2, t, and F Distributions
6.3: The Sample Mean and the Sample Variance
6.4: Problems
CHAPTER 7: Survey Sampling
7.1: Introduction
7.2: Population Parameters
7.3: Simple Random Sampling
7.4: Estimation of a Ratio
7.5: Stratified Random Sampling
7.6: Concluding Remarks
7.7: Problems
CHAPTER 8: Estimation of Parameters and Fitting of Probability Distributions
8.1: Introduction
8.2: Fitting the Poisson Distribution to Emissions of Alpha Particles
8.3: Parameter Estimation
8.4: The Method of Moments
8.5: The Method of Maximum Likelihood
8.6: The Bayesian Approach to Parameter Estimation
8.7: Efficiency and the Cramér-Rao Lower Bound
8.8: Sufficieny
8.9: Concluding Remarks
8.10: Problems
CHAPTER 9: Testing Hypotheses and Assessing Goodness of Fit
9.1: Introduction
9.2: The Neyman-Pearson Paradigm
9.3: The Duality of Confidence Intervals and Hypothesis Tests
9.4: Generalized Likelihood Ratio Tests
9.5: Likelihood Ratio Tests for the Multinomial Distribution
9.6: The Poisson Dispersion Test
9.7: Hanging Rootograms
9.8: Probability Plots
9.9: Tests for Normality
9.10: Concluding Remarks
9.11: Problems
CHAPTER 10: Summarizing Data
10.1: Introduction
10.2: Methods Based on the Cumulative Distribution Function
10.3: Histograms, Density Curves, and Stem-and-Leaf Plots
10.4: Measures of Location
10.5: Measures of Dispersion
10.6: Boxplots
10.7: Exploring Relationships with Scatterplots
10.8: Concluding Remarks
10.9: Problems
CHAPTER 11: Comparing Two Samples
11.1: Introduction
11.2: Comparing Two Independent Samples
11.3: Comparing Paired Samples
11.4: Experimental Design
11.5: Concluding Remarks
11.6: Problems
CHAPTER 12: The Analysis of Variance
12.1: Introduction
12.2: The One-Way Layout
12.3: The Two-Way Layout
12.4: Concluding Remarks
12.5: Problems
CHAPTER 13: The Analysis of Categorical Data
13.1: Introduction
13.2: Fisher’s Exact Test
13.3: The Chi-Square Test of Homogeneity
13.4: The Chi-Square Test of Independence
13.5: Matched-Pairs Designs
13.6: Odds Ratios
13.7: Concluding Remarks
13.8: Problems
CHAPTER 14: Linear Least Squares
14.1: Introduction
14.2: Simple Linear Regression
14.3: The Matrix Approach to Linear Least Squares
14.4: Statistical Properties of Least Squares Estimates
14.5: Multiple Linear Regression—An Example
14.6: Conditional Inference, Unconditional Inference, and the Bootstrap
14.7: Local Linear Smoothing
14.8: Concluding Remarks
14.9: Problems
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