Tabla de Contenidos: “…POSICIONAMIENTO DE UNIVERSIDADES VENEZOLANAS EN DOS RANKINGS (...) -- 4. ALGUNAS TESIS DOCTORALES RELACIONADAS CON LA ESTRUCTURA OPERATIVO- (...) -- 5. …”

Tabla de Contenidos: “…Mixture models for categorical data -- 2.1.1. Mixture of ranking data…”

“…SEO is an integral part of getting a site to rank in the various search engines in order to attract potential customers. …”

“…Search Engine Optimization For Dummies shows website owners, developers, and search engine optimizers (SEOs) how to create a website that ranks at the top of search engines and has high-volume traffic, while answering the essential question of "how do I get people to visit my site?" …”

“…Note: Per the Penguin Policy 2.0 update, some of the tasks in Chapter 6 may present a risk to Google page rank. Please read the latest policy update from Google to know fully what will work best for increasing and maintaining Google Page Rank…”

“…The text define the role of the Emperor, the ranks of the members of his entourage and the status of the court servants in a complex, symbolic system of relations. …”

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“…Can governments elevate their broadband performance rankings? This paper aims to identify the factors that are significant in driving broadband penetration…”

“…The larger issue for Latino leaders to consider is whether they can learn from the experiences of their black colleagues as they move into the higher ranks of organizations…”

Tabla de Contenidos: “…Geometric Mean -- Chapter 2 Linear Algebraic Systems -- 2.1 Problem Definition and Vector Spaces -- 2.1.1 Vector Spaces in Tomographic Radiometric Inversion -- 2.2 Rotations -- 2.3 Projection Matrixes and Data-Filtering -- 2.3.1 Projections and Commercial FM Radio -- 2.4 Singular Value Decomposition (SVD) and Subspaces -- 2.4.1 How to Choose the Rank of A? -- 2.5 QR and Cholesky Factorization -- 2.6 Power Method for Leading Eigenvectors -- 2.7 Least Squares Solution of Overdetermined Linear Equations -- 2.8 Efficient Implementation of the LS Solution -- 2.9 Iterative Methods -- Chapter 3 Random Variables in Brief -- 3.1 Probability Density Function (pdf), Moments, and Other Useful Properties -- 3.2 Convexity and Jensen Inequality -- 3.3 Uncorrelatedness and Statistical Independence -- 3.4 Real-Valued Gaussian Random Variables…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Brief Contents -- Contents -- Examples and Applications -- Preface -- Part I: The Linear Regression Model -- CHAPTER 1 Econometrics -- 1.1 Introduction -- 1.2 The Paradigm of Econometrics -- 1.3 The Practice of Econometrics -- 1.4 Microeconometrics and Macroeconometrics -- 1.5 Econometric Modeling -- 1.6 Plan of the Book -- 1.7 Preliminaries -- 1.7.1 Numerical Examples -- 1.7.2 Software and Replication -- 1.7.3 Notational Conventions -- CHAPTER 2 The Linear Regression Model -- 2.1 Introduction -- 2.2 The Linear Regression Model -- 2.3 Assumptions of the Linear Regression Model -- 2.3.1 Linearity of the Regression Model -- 2.3.2 Full Rank -- 2.3.3 Regression -- 2.3.4 Homoscedastic and Nonautocorrelated Disturbances -- 2.3.5 Data Generating Process for the Regressors -- 2.3.6 Normality -- 2.3.7 Independence and Exogeneity -- 2.4 Summary and Conclusions -- CHAPTER 3 Least Squares Regression -- 3.1 Introduction -- 3.2 Least Squares Regression -- 3.2.1 The Least Squares Coefficient Vector -- 3.2.2 Application: An Investment Equation -- 3.2.3 Algebraic Aspects of the Least Squares Solution -- 3.2.4 Projection -- 3.3 Partitioned Regression and Partial Regression -- 3.4 Partial Regression and Partial Correlation Coefficients -- 3.5 Goodness of Fit and the Analysis of Variance -- 3.5.1 The Adjusted R-Squared and a Measure of Fit -- 3.5.2 R-Squared and the Constant Term in the Model -- 3.5.3 Comparing Models -- 3.6 Linearly Transformed Regression -- 3.7 Summary and Conclusions -- CHAPTER 4 Estimating the Regression Model by Least Squares -- 4.1 Introduction -- 4.2 Motivating Least Squares -- 4.2.1 Population Orthogonality Conditions -- 4.2.2 Minimum Mean Squared Error Predictor -- 4.2.3 Minimum Variance Linear Unbiased Estimation -- 4.3 Statistical Properties of the Least Squares Estimator…”

Tabla de Contenidos: “…7.5.2 The Fisher Information -- 7.6 Point Estimation -- 7.6.1 Maximum Likelihood Estimation -- 7.6.2 Method of Moments Estimator -- 7.7 Comparison of Estimators -- 7.7.1 Unbiased Estimators -- 7.7.2 Improving Unbiased Estimators -- 7.8 Confidence Intervals -- 7.9 Testing Statistical Hypotheses-The Preliminaries -- 7.10 The Neyman-Pearson Lemma -- 7.11 Uniformly Most Powerful Tests -- 7.12 Uniformly Most Powerful Unbiased Tests -- 7.12.1 Tests for the Means: One- and Two-Sample t-Test -- 7.13 Likelihood Ratio Tests -- 7.13.1 Normal Distribution: One-Sample Problems -- 7.13.2 Normal Distribution: Two-Sample Problem for the Mean -- 7.14 Behrens-Fisher Problem -- 7.15 Multiple Comparison Tests -- 7.15.1 Bonferroni's Method -- 7.15.2 Holm's Method -- 7.16 The EM Algorithm* -- 7.16.1 Introduction -- 7.16.2 The Algorithm -- 7.16.3 Introductory Applications -- 7.17 Further Reading -- 7.17.1 Early Classics -- 7.17.2 Texts from the Last 30 Years -- 7.18 Complements, Problems, and Programs -- Chapter 8 Nonparametric Inference -- 8.1 Introduction -- 8.2 Empirical Distribution Function and Its Applications -- 8.2.1 Statistical Functionals -- 8.3 The Jackknife and Bootstrap Methods -- 8.3.1 The Jackknife -- 8.3.2 The Bootstrap -- 8.3.3 Bootstrapping Simple Linear Model* -- 8.4 Non-parametric Smoothing -- 8.4.1 Histogram Smoothing -- 8.4.2 Kernel Smoothing -- 8.4.3 Nonparametric Regression Models* -- 8.5 Non-parametric Tests -- 8.5.1 The Wilcoxon Signed-Ranks Test -- 8.5.2 The Mann-Whitney test -- 8.5.3 The Siegel-Tukey Test -- 8.5.4 The Wald-Wolfowitz Run Test -- 8.5.5 The Kolmogorov-Smirnov Test -- 8.5.6 Kruskal-Wallis Test* -- 8.6 Further Reading -- 8.7 Complements, Problems, and Programs -- Chapter 9 Bayesian Inference -- 9.1 Introduction -- 9.2 Bayesian Probabilities -- 9.3 The Bayesian Paradigm for Statistical Inference…”