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Test Bank for Mathematical Statistics with Applications (7th Edition) by Dennis Wackerly

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  • ISBN-10:  495110817 / ISBN-13:  9780495110811
  • Ebook Details

    • Edition: 7th edition
    • Format: Downloadable ZIP Fille
    • Resource Type : Testbank
    • Publication: 2008
    • Duration: Unlimited downloads
    • Delivery: Instant Download
     

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    Table of content:

    Half Title
    Title
    Statement
    Copyright
    Contents
    Preface
    Note to the Student
    Ch 1: What Is Statistics?
    1.1: Introduction
    1.2: Characterizing a Set of Measurements: Graphical Methods
    1.3: Characterizing a Set of Measurements: Numerical Methods
    1.4: How Inferences Are Made
    1.5: Theory and Reality
    1.6: Summary
    Ch 1: References and Further Readings
    Ch 1: Supplementary Exercises
    Ch 2: Probability
    2.1: Introduction
    2.2: Probability and Inference
    2.3: A Review of Set Notation
    2.4: A Probabilistic Model for an Experiment: The Discrete Case
    2.5: Calculating the Probability of an Event: The Sample-Point Method
    2.6: Tools for Counting Sample Points
    2.7: Conditional Probability and the Independence of Events
    2.8: Two Laws of Probability
    2.9: Calculating the Probability of an Event: The Event-Composition Method
    2.10: The Law of Total Probability and Bayes’ Rule
    2.11: Numerical Events and Random Variables
    2.12: Random Sampling
    2.13: Summary
    Ch 2: References and Further Readings
    Ch 2: Supplementary Exercises
    Ch 3: Discrete Random Variables and Their Probability Distributions
    3.1: Basic Definition
    3.2: The Probability Distribution for a Discrete Random Variable
    3.3: The Expected Value of a Random Variable or a Function of a Random Variable
    3.4: The Binomial Probability Distribution
    3.5: The Geometric Probability Distribution
    3.6: The Negative Binomial Probability Distribution (Optional)
    3.7: The Hypergeometric Probability Distribution
    3.8: The Poisson Probability Distribution
    3.9: Moments and Moment-Generating Functions
    3.10: Probability-Generating Functions (Optional)
    3.11: Tchebysheff’s Theorem
    3.12: Summary
    Ch 3: References and Further Readings
    Ch 3: Supplementary Exercises
    Ch 4: Continuous Variables and Their Probability Distributions
    4.1: Introduction
    4.2: The Probability Distribution for a Continuous Random Variable
    4.3: Expected Values for Continuous Random Variables
    4.4: The Uniform Probability Distribution
    4.5: The Normal Probability Distribution
    4.6: The Gamma Probability Distribution
    4.7: The Beta Probability Distribution
    4.8: Some General Comments
    4.9: Other Expected Values
    4.10: Tchebysheff’s Theorem
    4.11: Expectations of Discontinuous Functions and Mixed Probability Distributions (Optional)
    4.12: Summary
    Ch 4: References and Further Readings
    Ch 4: Supplementary Exercises
    Ch 5: Multivariate Probability Distributions
    5.1: Introduction
    5.2: Bivariate and Multivariate Probability Distributions
    5.3: Marginal and Conditional Probability Distributions
    5.4: Independent Random Variables
    5.5: The Expected Value of a Function of Random Variables
    5.6: Special Theorems
    5.7: The Covariance of Two Random Variables
    5.8: The Expected Value and Variance of Linear Functions of Random Variables
    5.9: The Multinomial Probability Distribution
    5.10: The Bivariate Normal Distribution (Optional)
    5.11: Conditional Expectations
    5.12: Summary
    Ch 5: References and Further Readings
    Ch 5: Supplementary Exercises
    Ch 6: Functions of Random Variables
    6.1: Introduction
    6.2: Finding the Probability Distribution of a Function of Random Variables
    6.3: The Method of Distribution Functions
    6.4: The Method of Transformations
    6.5: The Method of Moment-Generating Functions
    6.6: Multivariable Transformations Using Jacobians (Optional)
    6.7: Order Statistics
    6.8: Summary
    Ch 6: References and Further Readings
    Ch 6: Supplementary Exercises
    Ch 7: Sampling Distributions and the Central Limit Theorem
    7.1: Introduction
    7.2: Sampling Distributions Related to the Normal Distribution
    7.3: The Central Limit Theorem
    7.4: A Proof of the Central Limit Theorem (Optional)
    7.5: The Normal Approximation to the Binomial Distribution
    7.6: Summary
    Ch 7: References and Further Readings
    Ch 7: Supplementary Exercises
    Ch 8: Estimation
    8.1: Introduction
    8.2: The Bias and Mean Square Error of Point Estimators
    8.3: Some Common Unbiased Point Estimators
    8.4: Evaluating the Goodness of a Point Estimator
    8.5: Confidence Intervals
    8.6: Large-Sample Confidence Intervals
    8.7: Selecting the Sample Size
    8.8: Small-Sample Confidence Intervals for μ and μ1 − μ2
    8.9: Confidence Intervals for σ 2
    8.10: Summary
    Ch 8: References and Further Readings
    Ch 8: Supplementary Exercises
    Ch 9: Properties of Point Estimators and Methods of Estimation
    9.1: Introduction
    9.2: Relative Efficiency
    9.3: Consistency
    9.4: Sufficiency
    9.5: The Rao–Blackwell Theorem and Minimum-Variance Unbiased Estimation
    9.6: The Method of Moments
    9.7: The Method of Maximum Likelihood
    9.8: Some Large-Sample Properties of Maximum-Likelihood Estimators (Optional)
    9.9: Summary
    Ch 9: References and Further Readings
    Ch 10: Supplementary Exercises
    Ch 10: Hypothesis Testing
    10.1: Introduction
    10.2: Elements of a Statistical Test
    10.3: Common Large-Sample Tests
    10.4: Calculating Type II Error Probabilities and Finding the Sample Size for Z Tests
    10.5: Relationships Between Hypothesis-Testing Procedures and Confidence Intervals
    10.6: Another Way to Report the Results of a Statistical Test: Attained Significance Levels, or p-Va
    10.7: Some Comments on the Theory of Hypothesis Testing
    10.8: Small-Sample Hypothesis Testing for μ and μ1 − μ2
    10.9: Testing Hypotheses Concerning Variances
    10.10: Power of Tests and the Neyman–Pearson Lemma
    10.11: Likelihood Ratio Tests
    10.12: Summary
    Ch 10: References and Further Readings
    Ch 10: Supplementary Exercises
    Ch 11: Linear Models and Estimation by Least Squares
    11.1: Introduction
    11.2: Linear Statistical Models
    11.3: The Method of Least Squares
    11.4: Properties of the Least-Squares Estimators: Simple Linear Regression
    11.5: Inferences Concerning the Parameters βi
    11.6: Inferences Concerning Linear Functions of the Model Parameters: Simple Linear Regression
    11.7: Predicting a Particular Value of Y by Using Simple Linear Regression
    11.8: Correlation
    11.9: Some Practical Examples
    11.10: Fitting the Linear Model by Using Matrices
    11.11: Linear Functions of the Model Parameters: Multiple Linear Regression
    11.12: Inferences Concerning Linear Functions of the Model Parameters: Multiple Linear Regression
    11.13: Predicting a Particular Value of Y by Using Multiple Regression
    11.14: A Test for H0: βg+1 = βg+2 = ··· = βk = 0
    11.15: Summary and Concluding Remarks
    Ch 11: References and Further Readings
    Ch 11: Supplementary Exercises
    Ch 12: Considerations in Designing Experiments
    12.1: The Elements Affecting the Information in a Sample
    12.2: Designing Experiments to Increase Accuracy
    12.3: The Matched-Pairs Experiment
    12.4: Some Elementary Experimental Designs
    12.5: Summary
    Ch 12: References and Further Readings
    Ch 12: Supplementary Exercises
    Ch 13: The Analysis of Variance
    13.1: Introduction
    13.2: The Analysis of Variance Procedure
    13.3: Comparison of More Than Two Means: Analysis of Variance for a One-Way Layout
    13.4: An Analysis of Variance Table for a One-Way Layout
    13.5: A Statistical Model for the One-Way Layout
    13.6: Proof of Additivity of the Sums of Squares and E(MST) for a One-Way Layout (Optional)
    13.7: Estimation in the One-Way Layout
    13.8: A Statistical Model for the Randomized Block Design
    13.9: The Analysis of Variance for a Randomized Block Design
    13.10 Estimation in the Randomized Block Design
    13.11: Selecting the Sample Size
    13.12: Simultaneous Confidence Intervals for More Than One Parameter
    13.13: Analysis of Variance Using Linear Models
    13.14: Summary
    Ch 13: References and Further Readings
    Ch 13: Supplementary Exercises
    Ch 14: Analysis of Categorical Data
    14.1: A Description of the Experiment
    14.2: The Chi-Square Test
    14.3: A Test of a Hypothesis Concerning Specified Cell Probabilities: A Goodness-of-Fit Test
    14.4: Contingency Tables
    14.5: r × c Tables with Fixed Row or Column Totals
    14.6: Other Applications
    14.7: Summary and Concluding Remarks
    Ch 14: References and Further Readings
    Ch 14: Supplementary Exercises
    Ch 15: Nonparametric Statistics
    15.1: Introduction
    15.2: A General Two-Sample Shift Model
    15.3: The Sign Test for a Matched-Pairs Experiment
    15.4: The Wilcoxon Signed-Rank Test for a Matched-Pairs Experiment
    15.5: Using Ranks for Comparing Two Population Distributions: Independent Random Samples
    15.6: The Mann–Whitney U Test: Independent Random Samples
    15.7: The Kruskal–Wallis Test for the One-Way Layout
    15.8: The Friedman Test for Randomized Block Designs
    15.9: The Runs Test: A Test for Randomness
    15.10: Rank Correlation Coefficient
    15.11: Some General Comments on Nonparametric Statistical Tests
    Ch 15: References and Further Readings
    Ch 15: Supplementary Exercises
    Ch 16: Introduction to Bayesian Methods for Inference
    16.1: Introduction
    16.2: Bayesian Priors, Posteriors, and Estimators
    16.3: Bayesian Credible Intervals
    16.4: Bayesian Tests of Hypotheses
    16.5: Summary and Additional Comments
    Ch 16: References and Further Readings
    Appendix 1: Matrices and Other Useful Mathematical Results
    Appendix 2: Common Probability Distributions, Means, Variances, and Moment-Generating Functions
    Appendix 3: Tables
    Answers
    Index
    BES-1
    BES-2

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