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Sold Out # Mathematical Statistics with Applications

by ; ;
Edition: 7th
Format: Hardcover
Pub. Date: 10/10/2007
Publisher(s): Cengage Learning

## Summary

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.

 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 Table of Contents provided by Publisher. All Rights Reserved.

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