Modern mathematical statistics with applications

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010101665	QA276 D484 2007	Open Access Book	Book	Searching... Unknown
Searching... SPACE KL Main Library	30000010079868	QA276 D484 2007	Open Access Book	Book	Searching... Unknown

Many mathematical statistics texts are heavily oriented toward a rigorous mathematical development of probability and statistics, without emphasizing contemporary statistical practice. MODERN MATHEMATICAL STATISTICS WITH APPLICATIONS strikes a balance between mathematical foundations and statistical practice. Accomplished authors Jay Devore and Ken Berk first engage students with real-life problems and scenarios and then provide them with both foundational context and theory. This book follows the spirit of the Committee on the Undergraduate Program in Mathematics (CUPM) recommendation that every math student should study statistics and probability with an emphasis on data analysis.

1 Overview And Descriptive Statistics

Introduction

Populations, Samples, and Processes

Pictorial and Tabular Methods in Descriptive Statistics

Measures of Location

Measures of Variability

2 Probability

Introduction

Sample Spaces and Events

Axioms, Interpretations, and Properties of Probability

Counting Techniques

Conditional Probability

Independence

3 Discrete Random Variables And Probability Distributions

Introduction

Random Variables

Probability Distributions for Discrete Random Variables

Expected Values of Discrete Random Variables

Moments and Moment Generating Functions

The Binomial Probability Distribution

The Hypergeometric and Negative Binomial Distributions

The Poisson Probability Distribution

4 Continuous Random Variables And Probability Distributions

Introduction

Probability Density Functions and Cumulative Distribution Functions

Expected Values and Moment Generating Functions

The Normal Distribution

The Gamma Distribution and Its Relatives

Other Continuous Distributions

Probability Plots

Transformations of a Random Variable

5 Joint Probability Distributions

Introduction

Jointly Distributed Random Variables

Expected Values, Covariance, and Correlation

Conditional Distributions

Transformations of Random Variables

Order Statistics

6 Statistics And Sampling Distributions

Introduction

Statistics and Their Distributions

The Distribution of the Sample Mean

The Distribution of a Linear Combination

Distributions Based on a Normal Random Sample

Appendix

7 Point Estimation

Introduction

Some General Concepts of Point Estimation

Methods of Point Estimation

Sufficiency

Information and Efficiency

8 Statistical Intervals Based On A Single Sample

Introduction

Basic Properties of Confidence Intervals

Large-Sample Confidence Intervals for a Population Mean and Proportion

Intervals Based on a Normal Population Distribution

Confidence Intervals for the Variance and Standard Deviation of a Normal Population

Bootstrap Confidence Intervals

9 Tests Of Hypotheses Based On A Single Sample

Introduction

Hypotheses and Test Procedures

Tests About a Population Mean

Tests Concerning a Population Proportion

P-Values

Some Comments on Selecting a Test Procedure

10 Inferences Based On Two Samples

Introduction

z Tests and Confidence Intervals for a Difference between Two Population Means

The Two-Sample t Test and Confidence Interval

Analysis of Paired Data

Inferences about Two Population Proportions

Inferences about Two Population Variances

Comparisons Using the Bootstrap and Permutation Methods

11 The Analysis Of Variance

Introduction

Single-Factor ANOVA

Multiple Comparisons in ANOVA

Available:*

On Order

Summary

Summary

Table of Contents