Diff of /trunk/NationalProblemLibrary/Textbooks

Revision 466		Revision 467
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41	6 >>> Options	41	6 >>> Options
42	6.1 >>> Introduction to Options	42	6.1 >>> Introduction to Options
43	6.2 >>> Hedging Strategies	43	6.2 >>> Hedging Strategies
44	6.3 >>> Binomial Trees	44	6.3 >>> Binomial Trees
45		45
		46	TitleText('Mathematical Statistics')
		47	EditionText('6')
		48	AuthorText('Wackerly, Mendenhall, Scheaffer')
		49
		50	1 >>> What Is Statistics?
		51	1.1 >>> Introduction
		52	1.2 >>> Characterizing a Set of Measurements: Graphical Methods
		53	1.3 >>> Characterizing a Set of Measurements: Numerical Methods
		54	1.4 >>> How Inferences Are Made
		55	1.5 >>> Theory and Reality
		56	1.6 >>> Summary
		57	2 >>> Probability
		58	2.1 >>> Introduction
		59	2.2 >>> Probability and Inference
		60	2.3 >>> A Review of Set Notation
		61	2.4 >>> A Probabilistic Model for an Experiment: The Discrete Case
		62	2.5 >>> Calculating the Probability of an Event: The Sample-Point Method
		63	2.6 >>> Tools for Counting Sample Points
		64	2.7 >>> Conditional Probability and the Independence of Events
		65	2.8 >>> Two Laws of Probability
		66	2.9 >>> Calculating the Probability of an Event: The Event-Composition Methods
		67	2.10 >>> The Law of Total Probability and Bayes's Rule
		68	2.11 >>> Numerical Events and Random Variables
		69	2.12 >>> Random Sampling
		70	2.13 >>> Summary
		71	3 >>> Discrete Random Variables and Their Probability Distributions
		72	3.1 >>> Basic Definition
		73	3.2 >>> The Probability Distribution for Discrete Random Variable
		74	3.3 >>> The Expected Value of Random Variable or a Function of Random Variable
		75	3.4 >>> The Binomial Probability Distribution
		76	3.5 >>> The Geometric Probability Distribution
		77	3.6 >>> The Negative Binomial Probability Distribution
		78	3.7 >>> The Hypergeometric Probability Distribution
		79	3.8 >>> Moments and Moment-Generating Functions
		80	3.9 >>> Probability-Generating Functions
		81	3.10 >>> Tchebysheff's Theorem
		82	3.11 >>> Summary
		83	4 >>> Continuous Random Variables and Their Probability Distributions
		84	4.1 >>> Introduction
		85	4.2 >>> The Probability Distribution for Continuous Random Variable
		86	4.3 >>> The Expected Value for Continuous Random Variable
		87	4.4 >>> The Uniform Probability Distribution
		88	4.5 >>> The Normal Probability Distribution
		89	4.6 >>> The Gamma Probability Distribution
		90	4.7 >>> The Beta Probability Distribution
		91	4.8 >>> Some General Comments
		92	4.9 >>> Other Expected Values
		93	4.10 >>> Tchebysheff's Theorem
		94	4.11 >>> Expectations of Discontinuous Functions and Mixed Probability Distributions
		95	4.12 >>> Summary
		96	5 >>> Multivariate Probability Distributions
		97	5.1 >>> Introduction
		98	5.2 >>> Bivariate and Multivariate Probability Distributions
		99	5.3 >>> Independent Random Variables
		100	5.4 >>> The Expected Value of a Function of Random Variables
		101	5.5 >>> Special Theorems
		102	5.6 >>> The Covariance of Two Random Variables
		103	5.7 >>> The Expected Value and Variance of Linear Functions of Random Variables
		104	5.8 >>> The Multinomial Probability Distribution
		105	5.9 >>> The Bivariate Normal Distribution
		106	5.10 >>> Conditional Expectations
		107	5.11 >>> Summary
		108	6 >>> Functions of Random Variables
		109	6.1 >>> Introductions
		110	6.2 >>> Finding the Probability Distribution of a Function of Random Variables
		111	6.3 >>> The Method of Distribution Functions
		112	6.4 >>> The Methods of Transformations
		113	6.5 >>> Multivariable Transformations Using Jacobians
		114	6.6 >>> Order Statistics
		115	6.7 >>> Summary
		116	7 >>> Sampling Distributions and the Central Limit Theorem
		117	7.1 >>> Introduction
		118	7.2 >>> Sampling Distributions Related to the Normal Distribution
		119	7.3 >>> The Central Limit Theorem
		120	7.4 >>> A Proof of the Central Limit Theorem
		121	7.5 >>> The Normal Approximation to the Binomial Distributions
		122	7.6 >>> Summary
		123	8 >>> Estimation
		124	8.1 >>> Introduction
		125	8.2 >>> The Bias and Mean Square Error of Point Estimators
		126	8.3 >>> Some Common Unbiased Point Estimators
		127	8.4 >>> Evaluating the Goodness of Point Estimator
		128	8.5 >>> Confidence Intervals
		129	8.6 >>> Large-Sample Confidence Intervals Selecting the Sample Size
		130	8.7 >>> Small-Sample Confidence Intervals for u and u1-u2
		131	8.8 >>> Confidence Intervals for o2
		132	8.9 >>> Summary
		133	9 >>> Properties of Point Estimators and Methods of Estimation
		134	9.1 >>> Introduction
		135	9.2 >>> Relative Efficiency
		136	9.3 >>> Consistency
		137	9.4 >>> Sufficiency
		138	9.5 >>> The Rao-Blackwell Theorem and Minimum-Variance Unbiased Estimation
		139	9.6 >>> The Method of Moments
		140	9.7 >>> The Method of Maximum Likelihood
		141	9.8 >>> Some Large-Sample Properties of MLEs
		142	9.9 >>> Summary
		143	10 >>> Hypothesis Testing
		144	10.1 >>> Introduction
		145	10.2 >>> Elements of a Statistical Test
		146	10.3 >>> Common Large-Sample Tests
		147	10.4 >>> Calculating Type II Error Probabilities and Finding the Sample Size for the Z Test
		148	10.5 >>> Relationships Between Hypothesis Testing Procedures and Confidence Intervals
		149	10.6 >>> Another Way to Report the Results of a Statistical Test: Attained Significance Levels or p-Values
		150	10.7 >>> Some Comments on the Theory of Hypothesis Testing
		151	10.8 >>> Small-Sample Hypothesis Testing for u and u1-u2
		152	10.9 >>> Testing Hypotheses Concerning Variances
		153	10.10 >>> Power of Test and the Neyman-Pearson Lemma
		154	10.11 >>> Likelihood Ration Test
		155	10.12 >>> Summary
		156	11 >>> Linear Models and Estimation by Least Squares
		157	11.1 >>> Introduction
		158	11.2 >>> Linear Statistical Models
		159	11.3 >>> The Method of Least Squares
		160	11.4 >>> Properties of the Least Squares Estimators for the Simple Linear Regression Model
		161	11.5 >>> Inference Concerning the Parameters BI
		162	11.6 >>> Inferences Concerning Linear Functions of the Model Parameters: Simple Linear Regression
		163	11.7 >>> Predicting a Particular Value of Y Using Simple Linear Regression
		164	11.8 >>> Correlation
		165	11.9 >>> Some Practical Examples
		166	11.10 >>> Fitting the Linear Model by Using Matrices
		167	11.11 >>> Properties of the Least Squares Estimators for the Multiple Linear Regression Model
		168	11.12 >>> Inferences Concerning Linear Functions of the Model Parameters: Multiple Linear Regression
		169	11.13 >>> Prediction a Particular Value of Y Using Multiple Regression
		170	11.14 >>> A Test for H0: Bg+1 + Bg+2 = ? = Bk = 0
		171	11.15 >>> Summary and Concluding Remarks
		172	12. >>> Considerations in Designing Experiments
		173	12.1 >>> The Elements Affecting the Information in a Sample
		174	12.2 >>> Designing Experiment to Increase Accuracy
		175	12.3 >>> The Matched Pairs Experiment
		176	12.4 >>> Some Elementary Experimental Designs
		177	12.5 >>> Summary
		178	13 >>> The Analysis of Variance
		179	13.1 >>> Introduction
		180	13.2 >>> The Analysis of Variance Procedure
		181	13.3 >>> Comparison of More than Two Means: Analysis of Variance for a One-way Layout
		182	13.4 >>> An Analysis of Variance Table for a One-Way Layout
		183	13.5 >>> A Statistical Model of the One-Way Layout
		184	13.6 >>> Proof of Additivity of the Sums of Squares and E (MST) for a One-Way Layout
		185	13.7 >>> Estimation in the One-Way Layout
		186	13.8 >>> A Statistical Model for the Randomized Block Design
		187	13.9 >>> The Analysis of Variance for a Randomized Block Design
		188	13.10 >>> Estimation in the Randomized Block Design
		189	13.11 >>> Selecting the Sample Size
		190	13.12 >>> Simultaneous Confidence Intervals for More than One Parameter
		191	13.13 >>> Analysis of Variance Using Linear Models
		192	13.14 >>> Summary
		193	14 >>> Analysis of Categorical Data
		194	14.1 >>> A Description of the Experiment
		195	14.2 >>> The Chi-Square Test
		196	14.3 >>> A Test of Hypothesis Concerning Specified Cell Probabilities: A Goodness-of-Fit Test
		197	14.4 >>> Contingency Tables
		198	14.5 >>> r x c Tables with Fixed Row or Column Totals
		199	14.6 >>> Other Applications
		200	14.7 >>> Summary and Concluding Remarks
		201	15. >>> Nonparametric Statistics
		202	15.1 >>> Introduction
		203	15.2 >>> A General Two-Sampling Shift Model
		204	15.3 >>> A Sign Test for a Matched Pairs Experiment
		205	15.4 >>> The Wilcoxon Signed-Rank Test for a Matched Pairs Experiment
		206	15.5 >>> The Use of Ranks for Comparing Two Population Distributions: Independent Random Samples
		207	15.6 >>> The Mann-Whitney U Test: Independent Random Samples
		208	15.7 >>> The Kruskal-Wallis Test for One-Way Layout
		209	15.8 >>> The Friedman Test for Randomized Block Designs
		210	15.9 >>> The Runs Test: A Test for Randomness
		211	15.10 >>> Rank Correlation Coefficient
		212	15.11 >>> Some General Comments on Nonparametric Statistical Test
		213	Appendix 1 >>> Matrices and Other Useful Mathematical Results
		214	Appendix 1.1 >>> Matrices and Matrix Algebra
		215	Appendix 1.2 >>> Addition of Matrices
		216	Appendix 1.3 >>> Multiplication of a Matrix by a Real Number
		217	Appendix 1.4 >>> Matrix Multiplication
		218	Appendix 1.5 >>> Identity Elements
		219	Appendix 1.6 >>> The Inverse of a Matrix
		220	Appendix 1.7 >>> The Transpose of a Matrix
		221	Appendix 1.8 >>> A Matrix Expression for a System of Simultaneous Linear Equations
		222	Appendix 1.9 >>> Inverting a Matrix
		223	Appendix 1.10 >>> Solving a System of Simultaneous Linear Equations
		224	Appendix 1.11 >>> Other Useful Mathematical Results
		225	Appendix 2 >>> Common Probability Distributions, Means, Variances, and Moment Generating Functions
		226	Appendix 2.1 >>> Discrete Distributions
		227	Appendix 2.2 >>> Continuous Distributions.
		228	Appendix 3. >>> Tables
		229	Appendix 3.1 >>> Binomial Probabilities
		230	Appendix 3.2 >>> Table of e-x
		231	Appendix 3.3 >>> Poisson Probabilities
		232	Appendix 3.4 >>> Normal Curve Areas
		233	Appendix 3.5 >>> Percentage Points of the t Distributions
		234	Appendix 3.6 >>> Percentage Points of the F Distributions
		235	Appendix 3.7 >>> Distribution of Function U
		236	Appendix 3.8 >>> Critical Values of T in the Wilcoxon Matched-Pairs, Signed-Ranks Test
		237	Appendix 3.9 >>> Distribution of the Total Number of Runs R in Sample Size (n1,n2); P(R < a)
		238	Appendix 3.10 >>> Critical Values of Pearman's Rank Correlation Coefficient
		239	Appendix 3.11 >>> Random Numbers

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