Mathematical Statistics with Applications 7th edition

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Dennis D. Wackerly, William Mendenhall III, and Richard L. Scheaffer
Publisher: Cengage Learning

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  • Chapter 1: What Is Statistics?
    • 1.1: Introduction
    • 1.2: Characterizing a Set of Measurements: Graphical Methods (5)
    • 1.3: Characterizing a Set of Measurements: Numerical Methods (8)
    • 1.4: How Inferences Are Made
    • 1.5: Theory and Reality
    • 1.6: Summary
    • 1: Supplementary Exercises (8)
    • 1: Concept Questions (10)

  • Chapter 2: Probability
    • 2.1: Introduction
    • 2.2: Probability and Inference
    • 2.3: A Review of Set Notation (6)
    • 2.4: A Probabilistic Model for an Experiment: The Discrete Case (9)
    • 2.5: Calculating the Probability of an Event: The Sample-Point Method (5)
    • 2.6: Tools for Counting Sample Points (20)
    • 2.7: Conditional Probability and the Independence of Events (8)
    • 2.8: Two Laws of Probability (14)
    • 2.9: Calculating the Probability of an Event: The Event-Composition Method (6)
    • 2.10: The Law of Total Probability and Bayes' Rule (6)
    • 2.11: Numerical Events and Random Variables (2)
    • 2.12: Random Sampling
    • 2.13: Summary
    • 2: Supplementary Exercises (20)
    • 2: Concept Questions (24)

  • Chapter 3: Discrete Random Variables and Their Probability Distributions
    • 3.1: Basic Definition
    • 3.2: The Probability Distribution for a Discrete Random Variable (6)
    • 3.3: The Expected Value of a Random Variable or a Function of a Random Variable (11)
    • 3.4: The Binomial Probability Distribution (17)
    • 3.5: The Geometric Probability Distribution (12)
    • 3.6: The Negative Binomial Probability Distribution (Optional) (7)
    • 3.7: The Hypergeometric Probability Distribution (9)
    • 3.8: The Poisson Probability Distribution (13)
    • 3.9: Moments and Moment-Generating Functions (10)
    • 3.10: Probability-Generating Functions (Optional) (2)
    • 3.11: Tchebysheff's Theorem (7)
    • 3.12: Summary
    • 3: Supplementary Exercises (19)
    • 3: Concept Questions (26)

  • Chapter 4: Continuous Variables and Their Probability Distributions
    • 4.1: Introduction
    • 4.2: The Probability Distribution for a Continuous Random Variable (10)
    • 4.3: Expected Values for Continuous Random Variables (9)
    • 4.4: The Uniform Probability Distribution (10)
    • 4.5: The Normal Probability Distribution (10)
    • 4.6: The Gamma Probability Distribution (13)
    • 4.7: The Beta Probability Distribution (7)
    • 4.8: Some General Comments
    • 4.9: Other Expected Values (5)
    • 4.10: Tchebysheff's Theorem (4)
    • 4.11: Expectations of Discontinuous Functions and Mixed Probability Distributions (Optional) (4)
    • 4.12: Summary
    • 4: Supplementary Exercises (18)
    • 4: Concept Questions (6)

  • Chapter 5: Multivariate Probability Distributions
    • 5.1: Introduction
    • 5.2: Bivariate and Multivariate Probability Distributions (10)
    • 5.3: Marginal and Conditional Probability Distributions (12)
    • 5.4: Independent Random Variables (15)
    • 5.5: The Expected Value of a Function of Random Variables
    • 5.6: Special Theorems (9)
    • 5.7: The Covariance of Two Random Variables (7)
    • 5.8: The Expected Value and Variance of Linear Functions of Random Variables (8)
    • 5.9: The Multinomial Probability Distribution (6)
    • 5.10: The Bivariate Normal Distribution (Optional)
    • 5.11: Conditional Expectations (6)
    • 5.12: Summary
    • 5: Supplementary Exercises (11)
    • 5: Concept Questions (6)

  • Chapter 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 (11)
    • 6.4: The Method of Transformations (7)
    • 6.5: The Method of Moment-Generating Functions (13)
    • 6.6: Multivariable Transformations Using Jacobians (Optional)
    • 6.7: Order Statistics (10)
    • 6.8: Summary
    • 6: Supplementary Exercises (12)
    • 6: Concept Questions

  • Chapter 7: Sampling Distributions and the Central Limit Theorem
    • 7.1: Introduction
    • 7.2: Sampling Distributions Related to the Normal Distribution (12)
    • 7.3: The Central Limit Theorem (12)
    • 7.4: A Proof of the Central Limit Theorem (Optional)
    • 7.5: The Normal Approximation to the Binomial Distribution (11)
    • 7.6: Summary
    • 7: Supplementary Exercises (9)
    • 7: Concept Questions (4)

  • Chapter 8: Estimation
    • 8.1: Introduction
    • 8.2: The Bias and Mean Square Error of Point Estimators (10)
    • 8.3: Some Common Unbiased Point Estimators
    • 8.4: Evaluating the Goodness of a Point Estimator (9)
    • 8.5: Confidence Intervals (6)
    • 8.6: Large-Sample Confidence Intervals (10)
    • 8.7: Selecting the Sample Size (5)
    • 8.8: Small-Sample Confidence Intervals for μ and μ1μ2 (9)
    • 8.9: Confidence Intervals for σ2 (5)
    • 8.10: Summary
    • 8: Supplementary Exercises (13)
    • 8: Concept Questions (10)

  • Chapter 9: Properties of Point Estimators and Methods of Estimation
    • 9.1: Introduction
    • 9.2: Relative Efficiency (4)
    • 9.3: Consistency (11)
    • 9.4: Sufficiency (10)
    • 9.5: The Rao–Blackwell Theorem and Minimum-Variance Unbiased Estimation (7)
    • 9.6: The Method of Moments (6)
    • 9.7: The Method of Maximum Likelihood (10)
    • 9.8: Some Large-Sample Properties of Maximum-Likelihood Estimators (Optional) (2)
    • 9.9: Summary
    • 9: Supplementary Exercises (5)
    • 9: Concept Questions

  • Chapter 10: Hypothesis Testing
    • 10.1: Introduction
    • 10.2: Elements of a Statistical Test (5)
    • 10.3: Common Large-Sample Tests (10)
    • 10.4: Calculating Type II Error Probabilities and Finding the Sample Size for Z Tests (4)
    • 10.5: Relationships Between Hypothesis-Testing Procedures and Confidence Intervals (3)
    • 10.6: Another Way to Report the Results of a Statistical Test: Attained Significance Levels, or p-Values (4)
    • 10.7: Some Comments on the Theory of Hypothesis Testing
    • 10.8: Small-Sample Hypothesis Testing for μ and μ1μ2 (9)
    • 10.9: Testing Hypotheses Concerning Variances (5)
    • 10.10: Power of Tests and the Neyman–Pearson Lemma (8)
    • 10.11: Likelihood Ratio Tests (3)
    • 10.12: Summary
    • 10: Supplementary Exercises (8)
    • 10: Concept Questions (10)

  • Chapter 11: Linear Models and Estimation by Least Squares
    • 11.1: Introduction
    • 11.2: Linear Statistical Models
    • 11.3: The Method of Least Squares (6)
    • 11.4: Properties of the Least-Squares Estimators: Simple Linear Regression (4)
    • 11.5: Inferences Concerning the Parameters βi (6)
    • 11.6: Inferences Concerning Linear Functions of the Model Parameters: Simple Linear Regression (4)
    • 11.7: Predicting a Particular Value of Y by Using Simple Linear Regression (3)
    • 11.8: Correlation (5)
    • 11.9: Some Practical Examples (3)
    • 11.10: Fitting the Linear Model by Using Matrices (2)
    • 11.11: Linear Functions of the Model Parameters: Multiple Linear Regression
    • 11.12: Inferences Concerning Linear Functions of the Model Parameters: Multiple Linear Regression (4)
    • 11.13: Predicting a Particular Value of Y by Using Multiple Regression (3)
    • 11.14: A Test for H0 : βg + 1 = βg + 2 = … = βk = 0 (8)
    • 11.15: Summary and Concluding Remarks
    • 11: Supplementary Exercises (7)
    • 11: Concept Questions (8)

  • Chapter 12: Considerations in Designing Experiments
    • 12.1: The Elements Affecting the Information in a Sample
    • 12.2: Designing Experiments to Increase Accuracy (4)
    • 12.3: The Matched-Pairs Experiment (5)
    • 12.4: Some Elementary Experimental Designs (6)
    • 12.5: Summary
    • 12: Supplementary Exercises (5)
    • 12: Concept Questions (4)

  • Chapter 13: The Analysis of Variance
    • 13.1: Introduction
    • 13.2: The Analysis of Variance Procedure (1)
    • 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 (7)
    • 13.5: A Statistical Model for the One-Way Layout (2)
    • 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 (8)
    • 13.8: A Statistical Model for the Randomized Block Design (2)
    • 13.9: The Analysis of Variance for a Randomized Block Design (6)
    • 13.10: Estimation in the Randomized Block Design (3)
    • 13.11: Selecting the Sample Size (2)
    • 13.12: Simultaneous Confidence Intervals for More Than One Parameter (3)
    • 13.13: Analysis of Variance Using Linear Models (2)
    • 13.14: Summary
    • 13: Supplementary Exercises (11)
    • 13: Concept Questions (4)

  • Chapter 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 (6)
    • 14.4: Contingency Tables (5)
    • 14.5: r × c Tables with Fixed Row or Column Totals (5)
    • 14.6: Other Applications
    • 14.7: Summary and Concluding Remarks
    • 14: Supplementary Exercises (6)
    • 14: Concept Questions (4)

  • Chapter 15: Nonparametric Statistics
    • 15.1: Introduction
    • 15.2: A General Two-Sample Shift Model
    • 15.3: The Sign Test for a Matched-Pairs Experiment (5)
    • 15.4: The Wilcoxon Signed-Rank Test for a Matched-Pairs Experiment (5)
    • 15.5: Using Ranks for Comparing Two Population Distributions: Independent Random Samples
    • 15.6: The Mann–Whitney U Test: Independent Random Samples (4)
    • 15.7: The Kruskal–Wallis Test for the One-Way Layout (4)
    • 15.8: The Friedman Test for Randomized Block Designs (5)
    • 15.9: The Runs Test: A Test for Randomness (3)
    • 15.10: Rank Correlation Coefficient (4)
    • 15.11: Some General Comments on Nonparametric Statistical Tests
    • 15: Supplementary Exercises (9)
    • 15: Concept Questions (8)

  • Chapter 16: Introduction to Bayesian Methods for Inference
    • 16.1: Introduction
    • 16.2: Bayesian Priors, Posteriors, and Estimators (7)
    • 16.3: Bayesian Credible Intervals (3)
    • 16.4: Bayesian Tests of Hypotheses (3)
    • 16.5: Summary and Additional Comments
    • 16: Concept Questions


In their bestselling Mathematical Statistics With Applications, 7th edition, 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 students discover the nature of statistics and understand its essential role in scientific research. The WebAssign component for this text engages students with immediate feedback, a complete eBook, and a question bank of end-of-section exercises.

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Group Quantity Questions
Chapter 1: What Is Statistics?
1.CQ 10 001 002 003 004 005 006 007 008 009 010
1.E 21 002 003 005 006 007 009 011 013 015 017 019 020 021 023 025 027 029 031 033 035 037
Chapter 2: Probability
2.CQ 24 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024
2.E 96 001 002 003 005 007 008 009 011 013 014 015 017 019 021 023 025 027 029 031 033 035 037 038 039 041 043 045 047 049 051 053 055 057 058 059 061 063 065 067 069 071 072 073 075 077 079 081 083 085 087 089 091 093 095 097 099 101 102 103 105 107 109 111 113 115 117 119 121 125 129 131 133 135 137 139 141 143 145 147 149 151 153 155 157 159 161 163 165 167 169 171 173 175 177 179 181
Chapter 3: Discrete Random Variables and Their Probability Distributions
3.CQ 26 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026
3.E 113 001 003 005 007 009 011 013 015 017 019 021 023 025 027 029 031 033 035 037 039 040 041 043 045 047 049 051 053 055 057 059 061 063 065 067 069 071 073 075 077 079 081 083 085 087 089 090 091 092 093 095 097 099 103 105 107 109 111 113 115 117 119 121 123 125 127 129 130 131 133 135 137 139 141 143 145 147 149 151 153 155 157 159 161 163 164 165 167 169 171 173 175 177 179 181 183 185 187 189 191 193 195 197 199 201 203 205 207 209 211 213 215 217
Chapter 4: Continuous Variables and Their Probability Distributions
4.CQ 6 001 002 003 004 005 006
4.E 90 001 003 005 007 009 011 013 015 017 019 021 023 025 027 029 031 033 035 037 039 041 043 045 047 049 051 053 055 057 059 061 063 065 067 069 071 073 075 077 081 089 091 093 095 097 099 101 103 105 107 109 111 123 125 127 129 131 133 135 137 139 141 143 145 147 149 151 153 155 156 157 158 161 163 165 167 169 171 173 175 177 179 181 183 185 187 189 191 193 195
Chapter 5: Multivariate Probability Distributions
5.CQ 6 001 002 003 004 005 006
5.E 84 001 003 004 005 007 009 011 013 015 017 019 021 023 025 027 029 031 033 035 037 039 041 043 045 047 049 051 053 055 057 059 061 063 065 067 069 071 072 073 075 077 079 081 083 085 087 089 091 093 095 097 099 101 103 105 107 109 111 113 115 117 119 121 123 125 126 127 133 135 137 139 141 143 145 147 149 151 153 155 157 158 161 165 166
Chapter 6: Functions of Random Variables
6.E 53 001 003 005 007 009 011 013 015 017 019 021 023 025 026 029 031 033 035 037 039 041 043 045 047 049 051 053 055 057 059 061 073 075 077 079 081 083 085 087 089 090 093 095 097 099 101 103 105 107 109 111 113 115
Chapter 7: Sampling Distributions and the Central Limit Theorem
7.CQ 4 001 002 003 004
7.E 44 009 011 013 015 016 019 021 031 033 035 037 039 042 043 045 047 049 051 053 055 057 059 061 063 067 069 071 073 075 077 079 080 083 085 087 089 091 093 095 097 099 101 103 105
Chapter 8: Estimation
8.CQ 10 001 002 003 004 005 006 007 008 009 010
8.E 67 002 003 005 007 009 011 013 015 017 019 021 023 025 027 029 031 033 035 037 039 041 043 045 047 049 050 056 057 059 061 063 064 065 067 069 071 073 075 077 079 081 083 085 086 087 088 089 091 093 095 097 099 101 103 104 105 107 109 111 113 115 117 119 121 123 125 126
Chapter 9: Properties of Point Estimators and Methods of Estimation
9.E 55 001 003 005 007 015 017 019 021 023 025 027 029 031 033 035 037 039 041 043 045 047 049 051 053 055 056 057 059 061 063 065 067 070 071 073 075 077 079 080 081 083 085 087 089 091 093 095 097 099 101 103 105 107 109 111
Chapter 10: Hypothesis Testing
10.CQ 10 001 002 003 004 005 006 007 008 009 010
10.E 59 002 003 005 006 007 017 019 021 023 025 027 029 031 033 035 037 039 041 043 045 047 049 051 053 055 057 061 063 065 067 069 071 073 075 077 079 081 083 085 087 089 091 093 095 097 099 101 103 105 107 109 115 117 119 121 123 125 127 129
Chapter 11: Linear Models and Estimation by Least Squares
11.CQ 8 001 002 003 004 005 006 007 008
11.E 55 001 003 005 009 011 013 016 017 019 021 023 025 027 029 031 033 035 037 039 041 043 045 047 051 053 055 057 059 061 063 065 067 069 071 073 074 075 077 078 079 080 081 083 085 087 089 091 093 095 097 099 101 103 105 107
Chapter 12: Considerations in Designing Experiments
12.CQ 4 001 002 003 004
12.E 20 001 003 005 007 009 011 013 015 017 019 021 022 023 025 027 029 031 033 035 037
Chapter 13: The Analysis of Variance
13.CQ 4 001 002 003 004
13.E 47 001 003 005 007 009 011 013 015 017 019 021 023 025 027 029 031 033 035 037 039 041 043 045 047 049 051 053 055 057 059 061 063 065 067 069 071 073 075 077 079 081 083 085 087 089 091 093
Chapter 14: Analysis of Categorical Data
14.CQ 4 001 002 003 004
14.E 22 001 003 005 007 009 011 013 015 017 019 021 023 025 027 029 031 033 035 037 039 041 043
Chapter 15: Nonparametric Statistics
15.CQ 8 001 002 003 004 005 006 007 008
15.E 39 001 003 005 007 009 011 013 015 017 019 021 023 025 027 029 031 033 035 037 039 041 043 045 047 049 051 053 055 057 059 061 063 065 067 069 071 073 075 077
Chapter 16: Introduction to Bayesian Methods for Inference
16.E 13 001 002 006 007 008 009 011 015 017 019 021 023 025
Total 1002