Mathematical Statistics with Applications - 7th Edition - by Dennis Wackerly, William Mendenhall, Richard L. Scheaffer - ISBN 9780495110811
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Mathematical Statistics with Applicatio...
7th Edition
Dennis Wackerly, William Mendenhall, Richard L. Scheaffer
Publisher: Cengage Learning
ISBN: 9780495110811

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Chapter 2.8 - Two Laws Of ProbabilityChapter 2.9 - Calculating The Probability Of An Event: The Event-composition MethodChapter 2.10 - The Law Of Total Probability And Bayes’ RuleChapter 2.11 - Numerical Events And Random VariablesChapter 3 - Discrete Random Variables And Their Probability DistributionsChapter 3.2 - The Probability Distribution For A Discrete Random VariableChapter 3.3 - The Expected Value Of A Random Variable Or A Function Of A Random VariableChapter 3.4 - The Binomial Probability DistributionChapter 3.5 - The Geometric Probability DistributionChapter 3.6 - The Negative Binomial Probability Distribution (optional)Chapter 3.7 - The Hypergeometric Probability DistributionChapter 3.8 - The Poisson Probability DistributionChapter 3.9 - Moments And Moment-generating FunctionsChapter 3.10 - Probability-generating Functions (optional)Chapter 3.11 - Tchebysheff’s TheoremChapter 4 - Continuous Variables And Their Probability DistributionsChapter 4.2 - The Probability Distribution For A Continuous Random VariableChapter 4.3 - Expected Values For Continuous Random VariablesChapter 4.4 - The Uniform Probability DistributionChapter 4.5 - The Normal Probability DistributionChapter 4.6 - The Gamma Probability DistributionChapter 4.7 - The Beta Probability DistributionChapter 4.9 - Other Expected ValuesChapter 4.10 - Tchebysheff’s TheoremChapter 4.11 - Expectations Of Discontinuous Functions And Mixed Probability Distributions (optional)Chapter 5 - Multivariate Probability DistributionsChapter 5.2 - Bivariate And Multivariate Probability DistributionsChapter 5.3 - Marginal And Conditional Probability DistributionsChapter 5.4 - Independent Random VariablesChapter 5.6 - Special TheoremsChapter 5.7 - The Covariance Of Two Random VariablesChapter 5.8 - The Expected Value And Variance Of Linear Functions Of Random VariablesChapter 5.9 - The Multinomial Probability DistributionChapter 5.10 - The Bivariate Normal Distribution (optional)Chapter 5.11 - Conditional ExpectationsChapter 6 - Functions Of Random VariablesChapter 6.3 - The Method Of Distribution FunctionsChapter 6.4 - The Method Of TransformationsChapter 6.5 - The Method Of Moment-generating FunctionsChapter 6.6 - Multivariable Transformations Using Jacobians (optional)Chapter 7 - Sampling Distributions And The Central Limit TheoremChapter 7.2 - Sampling Distributions Related To The Normal DistributionChapter 7.3 - The Central Limit TheoremChapter 7.5 - The Normal Approximation To The Binomial DistributionChapter 8 - EstimationChapter 8.2 - The Bias And Mean Square Error Of Point EstimatorsChapter 8.4 - Evaluating The Goodness Of A Point EstimatorChapter 8.5 - Confidence IntervalsChapter 8.6 - Large-sample Confidence IntervalsChapter 8.7 - Selecting The Sample SizeChapter 8.8 - Small-sample Confidence Intervals For μ And Μ1 − Μ2Chapter 8.9 - Confidence Intervals For σ 2Chapter 9 - Properties Of Point Estimators And Methods Of EstimationChapter 9.2 - Relative EfficiencyChapter 9.3 - ConsistencyChapter 9.4 - SufficiencyChapter 9.5 - The Rao–blackwell Theorem And Minimum-variance Unbiased EstimationChapter 9.6 - The Method Of MomentsChapter 9.7 - The Method Of Maximum LikelihoodChapter 9.8 - Some Large-sample Properties Of Maximum-likelihoodChapter 10 - Hypothesis TestingChapter 10.2 - Elements Of A Statistical TestChapter 10.3 - Common Large-sample TestsChapter 10.4 - Calculating Type Ii Error Probabilities And Finding The Sample Size For Z TestsChapter 10.5 - Relationships Between Hypothesis-testing Procedures And Confidence IntervalsChapter 10.6 - Another Way To Report The Results Of A Statistical Test:attained Significance Levels, Or P-valuesChapter 10.8 - Small-sample Hypothesis Testing For μ And Μ1 − Μ2Chapter 10.9 - Testing Hypotheses Concerning VariancesChapter 10.10 - Power Of Tests And The Neyman–pearson LemmaChapter 10.11 - Likelihood Ratio TestsChapter 11 - Linear Models And Estimation By Least SquaresChapter 11.3 - The Method Of Least SquaresChapter 11.4 - Properties Of The Least-squares Estimators: Simple Linear RegressionChapter 11.5 - Inferences Concerning The Parameters ΒiChapter 11.6 - Inferences Concerning Linear Functions Of The Model Parameters: Simple Linear RegressionChapter 11.7 - Predicting A Particular Value Of Y By Using Simple Linear RegressionChapter 11.8 - CorrelationChapter 11.9 - Some Practical ExamplesChapter 11.10 - Fitting The Linear Model By Using MatricesChapter 11.12 - Inferences Concerning Linear Functions Of The Model Parameters: Multiple Linear RegressionChapter 11.13 - Predicting A Particular Value Of Y By Using Multiple RegressionChapter 11.14 - A Test For H0 : Βg+1 = Βg+2 = ··· = Βk =0Chapter 12 - Considerations In Designing ExperimentsChapter 12.2 - Designing Experiments To Increase AccuracyChapter 12.3 - The Matched-pairs ExperimentChapter 12.4 - Some Elementary Experimental DesignsChapter 13 - The Analysis Of VarianceChapter 13.2 - The Analysis Of Variance ProcedureChapter 13.4 - An Analysis Of Variance Table For A One-way LayoutChapter 13.5 - A Statistical Model For The One-way LayoutChapter 13.7 - Estimation In The One-way LayoutChapter 13.8 - A Statistical Model For The Randomized Block DesignChapter 13.9 - The Analysis Of Variance For A Randomized Block DesignChapter 13.10 - Estimation In The Randomized Block DesignChapter 13.11 - Selecting The Sample SizeChapter 13.12 - Simultaneous Confidence Intervals For More Than One ParameterChapter 13.13 - Analysis Of Variance Using Linear ModelsChapter 14 - Analysis Of Categorical DataChapter 14.3 - A Test Of A Hypothesis Concerning Specified Cell Probabilities: A Goodness-of-fit TestChapter 14.4 - Contingency TablesChapter 14.5 - R × C Tables With Fixed Row Or Column TotalsChapter 15 - Nonparametric StatisticsChapter 15.3 - The Sign Test For A Matched-pairs ExperimentChapter 15.4 - The Wilcoxon Signed-rank Test For A Matched-pairs ExperimentChapter 15.6 - The Mann–whitney U Test: Independent Random SamplesChapter 15.7 - The Kruskal–wallis Test For The One-way LayoutChapter 15.8 - The Friedman Test For Randomized Block DesignsChapter 15.9 - The Runs Test: A Test For RandomnessChapter 15.10 - Rank Correlation CoefficientChapter 16.2 - Bayesian Priors, Posteriors, And Estimators

Book Details

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

11. Linear Models and Estimation by Least Sq

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More Editions of This Book

Corresponding editions of this textbook are also available below:
Mathematical statistics with applications
6 Edition
ISBN: 9780534377410
Mathematical Statistics with Applications
7 Edition
ISBN: 9781111798789

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