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Description:

In their bestselling MATHEMATICAL STATISTICS WITH APPLICATIONS, premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors' use of practical applications and excellent exercises helps you discover the nature of statistics and understand its essential role in scientific research.

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Book details

List price: $199.95 Edition: 7th Copyright year: 2008 Publisher: Brooks/Cole Publication date: 10/10/2007 Binding: Hardcover Pages: 944 Size: 7.50" wide x 9.25" long x 1.50" tall Weight: 3.740 Language: English

AuthorTable of Contents

Richard L. Scheaffer, Professor Emeritus of Statistics, University of Florida, received his Ph.D. in statistics from Florida State University. Accompanying a career of teaching, research and administration, Dr. Scheaffer has led efforts on the improvement of statistics education throughout the school and college curriculum. Co-author of five textbooks, he was one of the developers of the Quantitative Literacy Project that formed the basis of the data analysis strand in the curriculum standards of the National Council of Teachers of Mathematics. He also led the task force that developed the AP Statistics Program, for which he served as Chief Faculty Consultant. Dr. Scheaffer is a Fellow and past president of the American Statistical Association, a past chair of the Conference Board of the Mathematical Sciences, and an advisor on numerous statistics education projects.

What Is Statistics? Introduction

Characterizing a Set of Measurements: Graphical Methods

Characterizing a Set of Measurements: Numerical Methods

How Inferences Are Made

Theory and Reality

Summary

Probability

Introduction

Probability and Inference

A Review of Set Notation

A Probabilistic Model for an Experiment: The Discrete Case

Calculating the Probability of an Event: The Sample-Point Method

Tools for Counting Sample Points

Conditional Probability and the Independence of Events

Two Laws of Probability

Calculating the Probability of an Event: The Event-Composition Methods

The Law of Total Probability and Bayes's Rule

Numerical Events and Random Variables

Random Sampling

Summary

Discrete Random Variables and Their Probability Distributions

Basic Definition

The Probability Distribution for Discrete Random Variable

The Expected Value of Random Variable or a Function of Random Variable

The Binomial Probability Distribution

The Geometric Probability Distribution

The Negative Binomial Probability Distribution (Optional)

The Hypergeometric Probability Distribution

Moments and Moment-Generating Functions

Probability-Generating Functions (Optional)

Tchebysheff's Theorem

Summary

Continuous Random Variables and Their Probability Distributions

Introduction

The Probability Distribution for Continuous Random Variable

The Expected Value for Continuous Random Variable

The Uniform Probability Distribution

The Normal Probability Distribution

The Gamma Probability Distribution

The Beta Probability Distribution

Some General Comments

Other Expected Values

Tchebysheff's Theorem

Expectations of Discontinuous Functions and Mixed Probability Distributions (Optional)

Summary

Multivariate Probability Distributions

Introduction

Bivariate and Multivariate Probability Distributions

Independent Random Variables

The Expected Value of a Function of Random Variables

Special Theorems

The Covariance of Two Random Variables

The Expected Value and Variance of Linear Functions of Random Variables

The Multinomial Probability Distribution

The Bivariate Normal Distribution (Optional)

Conditional Expectations

Summary

Functions of Random Variables

Introductions

Finding the Probability Distribution of a Function of Random Variables

The Method of Distribution Functions

The Methods of Transformations

Multivariable Transformations Using Jacobians

Order Statistics

Summary

Sampling Distributions and the Central Limit Theorem

Introduction

Sampling Distributions Related to the Normal Distribution

The Central Limit Theorem

A Proof of the Central Limit Theorem (Optional)

The Normal Approximation to the Binomial Distributions

Summary

Estimation

Introduction

The Bias and Mean Square Error of Point Estimators

Some Common Unbiased Point Estimators

Evaluating the Goodness of Point Estimator

Confidence Intervals

Large-Sample Confidence Intervals Selecting the Sample Size

Small-Sample Confidence Intervals for u and u1-u2

Confidence Intervals for o2

Summary

Properties of Point Estimators and Methods of Estimation

Introduction

Relative Efficiency

Consistency

Sufficiency

The Rao-Blackwell Theorem and Minimum-Variance Unbiased Estimation

The Method of Moments

The Method of Maximum Likelihood

Some Large-Sample Properties of MLEs (Optional)

Summary

Hypothesis Testing

Introduction

Elements of a Statistical Test

Common Large-Sample Tests

Calculating Type II Error Probabilities and Finding the Sample Size for the Z Test

Relationships Between Hypothesis Testing Procedures and Confidence Intervals

Another Way to Report the Results of a Statistical Test: Attained Significance Levels or p-Values

Some Comments on the Theory of Hypothesis Testing

Small-Sample Hypothesis Testing for u and u1-u2

Testing Hypotheses Concerning Variances

Power of Test and the Neyman-Pearson Lemma

Likelihood Ration Test

Summary

Linear Models and Estimation by Least Sq

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