Tabla de Contenidos: “…Displaying the order in a group of numbers using tables and graphs -- Central tendency and variability -- Some key ingredients and inferential statistics : Z scores, the normal curve, sample versus population, and probability -- Introduction to hypothesis testing -- Hypothesis tests with means of samples -- Making sense of statistical significance : decision errors, effect size, and statistical power -- Introduction to t tests : single sample and dependent means -- The t test for independent means -- Introduction to the analysis of variance -- Factorial analysis of variance -- Correlation -- Prediction -- Chi-square tests -- Strategies when population distributions are not normal : data transformations and rank-order tests -- The general linear model and making sense of advanced statistical procedures in research articles…”

Tabla de Contenidos: “…a speed control remote with central steering wheel -- linear actuators -- large linear actuator -- small linear actuator -- linear actuators vs. pneumatics -- extension wires -- miscellaneous elements -- switch -- LED lights -- Chapter 15: the RC system -- overview of the LEGO RC systems -- RC proper system and its components -- the control unit -- the remote -- the steering attachment -- motors -- putting it all together -- RC system vs. …”

Tabla de Contenidos: “…Obtain Information from or about the Graph of a Function -- 2.3 Properties of Functions -- Identify Even and Odd Functions from a Graph -- Identify Even and Odd Functions from an Equation -- Use a Graph to Determine Where a Function is Increasing, Decreasing, or Constant -- Use a Graph to Locate Local Maxima and Local Minima -- Use a Graph to Locate the Absolute Maximum and the Absolute Minimum -- Use a Graphing Utility to Approximate Local Maxima and Local Minima and to Determine Where a Function Is Increasing or Decreasing -- Find the Average Rate of Change of a Function -- 2.4 Library of Functions -- Piecewise-defined Functions -- Graph the Functions Listed in the Library of Functions -- Analyze a Piecewise-defined Function -- 2.5 Graphing Techniques: Transformations -- Graph Functions Using Vertical and Horizontal Shifts -- Graph Functions Using Compressions and Stretches -- Graph Functions Using Reflections about the x-Axis and the y-Axis -- 2.6 Mathematical Models: Building Functions -- Build and Analyze Functions -- Chapter Review -- Chapter Test -- Cumulative Review -- Chapter Projects -- Chapter 3. Linear and Quadratic Functions -- 3.1 Properties of Linear Functions and Linear Models -- Graph Linear Functions -- Use Average Rate of Change to Identify Linear Functions -- Determine Whether a Linear Function Is Increasing, Decreasing, or Constant -- Build Linear Models from Verbal Descriptions -- 3.2 Building Linear Models from Data -- Draw and Interpret Scatter Plots -- Distinguish between Linear and Nonlinear Relations -- Use a Graphing Utility to Find the Line of Best Fit -- 3.3 Quadratic Functions and Their Properties -- Graph a Quadratic Function Using Transformations -- Identify the Vertex and Axis of Symmetry of a Parabola -- Graph a Quadratic Function Using Its Vertex, Axis, and Intercepts…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Journey from Statistics to Machine Learning -- Statistical terminology for model building and validation -- Machine learning -- Major differences between statistical modeling and machine learning -- Steps in machine learning model development and deployment -- Statistical fundamentals and terminology for model building and validation -- Bias versus variance trade-off -- Train and test data -- Machine learning terminology for model building and validation -- Linear regression versus gradient descent -- Machine learning losses -- When to stop tuning machine learning models -- Train, validation, and test data -- Cross-validation -- Grid search -- Machine learning model overview -- Summary -- Chapter 2: Parallelism of Statistics and Machine Learning -- Comparison between regression and machine learning models -- Compensating factors in machine learning models -- Assumptions of linear regression -- Steps applied in linear regression modeling -- Example of simple linear regression from first principles -- Example of simple linear regression using the wine quality data -- Example of multilinear regression - step-by-step methodology of model building -- Backward and forward selection -- Machine learning models - ridge and lasso regression -- Example of ridge regression machine learning -- Example of lasso regression machine learning model -- Regularization parameters in linear regression and ridge/lasso regression -- Summary -- Chapter 3: Logistic Regression Versus Random Forest -- Maximum likelihood estimation -- Logistic regression - introduction and advantages -- Terminology involved in logistic regression -- Applying steps in logistic regression modeling…”

Tabla de Contenidos: “…Introduction -- 1.1 The tradition of text collections - The anthropological-linguistic motivation for this study -- 1.2 The psycholinguistic motivation for this study -- 1.3 From the psycholinguistic approach to linearization to research on the conceptual structure of -- Chapter 2. …”

Tabla de Contenidos: “…IntroductionGraphics AreasMajor ApplicationsGraphics APIsGraphics PipelineNumerical IssuesEfficiencyDesigning and Coding Graphics ProgramsMiscellaneous MathSets and MappingsSolving Quadratic EquationsTrigonometryVectorsCurves and SurfacesLinear InterpolationTrianglesRaster ImagesRaster DevicesImages, Pixels, and GeometryRGB ColorAlpha CompositingRay TracingThe Basic Ray-Tracing AlgorithmPerspectiveComputing Viewing RaysRay-Object IntersectionShadingA Ray-Tracing ProgramShadowsIdeal Specular ReflectionHistorical NotesLinear AlgebraDeterminantsMatricesComputing with Matrices and DeterminantsEigenvalues and Matrix DiagonalizationTransformation Matrices2D Linear Transformations3D Linear TransformationsTranslation and Affine TransformationsInverses of Transformation MatricesCoordinate TransformationsViewingViewing TransformationsProjective TransformationsPerspective ProjectionSome Properties of the Perspective TransformField-of-ViewThe Graphics PipelineRasterizationOperations Before and After RasterizationSimple AntialiasingCulling Primitives for EfficiencySignal ProcessingDigital Audio: Sampling in 1DConvolutionConvolution FiltersSignal Processing for ImagesSampling TheorySurface ShadingDiffuse ShadingPhong ShadingArtistic ShadingTexture MappingLooking Up Texture ValuesTexture Coordinate FunctionsAntialiasing Texture LookupsApplications of Texture MappingProcedural 3D TexturesData Structures for GraphicsTriangle MeshesScene GraphsSpatial Data StructuresBSP Trees for VisibilityTiling Multidimensional ArraysMore Ray TracingTransparency and RefractionInstancingConstructive Solid GeometryDistribution Ray TracingSamplingIntegrationContinuous ProbabilityMonte Carlo IntegrationChoosing Random PointsCurvesCurvesCurve PropertiesPolynomial PiecesPutting Pieces TogetherCubicsApproximating CurvesSummaryComputer AnimationPrinciples of AnimationKeyframingDeformationsCharacter AnimationPhysics-Based AnimationProcedural TechniquesGroups of ObjectsUsing Graphics HardwareHardware OverviewWhat Is Graphics HardwareHeterogeneous MultiprocessingGraphics Hardware Programming: Buffers, State, and ShadersState MachineBasic OpenGL Application LayoutGeometryA First Look at ShadersVertex Buffer ObjectsVertex Array ObjectsTransformation MatricesShading with Per-Vertex AttributesShading in the Fragment ProcessorMeshes and InstancingTexture ObjectsObject-Oriented Design for Graphics Hardware ProgrammingContinued LearningLightRadiometryTransport EquationPhotometryColorColorimetryColor SpacesChromatic AdaptationColor AppearanceVisual PerceptionVision ScienceVisual SensitivitySpatial VisionObjects, Locations, and EventsPicture PerceptionTone ReproductionClassificationDynamic RangeColorImage FormationFrequency-Based OperatorsGradient-Domain OperatorsSpatial OperatorsDivisionSigmoidsOther ApproachesNight TonemappingDiscussionImplicit ModelingImplicit Functions, Skeletal Primitives, and Summation BlendingRenderingSpace PartitioningMore on BlendingConstructive Solid GeometryWarpingPrecise Contact ModelingThe Blob TreeInteractive Implicit Modeling SystemsGlobal IlluminationParticle Tracing for Lambertian ScenesPath TracingAccurate Direct LightingReflection ModelsReal-World MaterialsImplementing Reflection ModelsSpecular Reflection ModelsSmooth-Layered ModelRough-Layered ModelComputer Graphics in GamesPlatformsLimited ResourcesOptimization TechniquesGame TypesThe Game Production ProcessVisualizationBackgroundData TypesHuman-Centered Design ProcessVisual Encoding PrinciplesInteraction PrinciplesComposite and Adjacent ViewsData ReductionExamples…”

Tabla de Contenidos: “…2.8 Problems References; Chapter 3 Linear Classifiers; 3.1 Introduction; 3.2 Linear Discriminant Functions and Decision Hyperplanes; 3.3 The Perceptron Algorithm; 3.4 Least Squares Methods; 3.5 Mean Square Estimation Revisited; 3.6 Logistic Discrimination; 3.7 Support Vector Machines; 3.8 Problems; References; Chapter 4 Nonlinear Classifiers; 4.1 Introduction; 4.2 The XOR Problem; 4.3 The Two-Layer Perceptron; 4.4 Three-Layer Perceptrons; 4.5 Algorithms Based on Exact Classification of the Training Set; 4.6 The Back propagation Algorithm; 4.7 Variations on the Back propagation Theme…”

Tabla de Contenidos: “…CÓMO Y POR QUÉ SER CRUDIVEGANO -- PÁGINA LEGAL -- ÍNDICE GENERAL -- FILOSOFÍA PERSONAL SOBRE CRUDIVEGANISMO -- ÍNDICE GENERAL -- AGRADECIMIENTOS -- PREFACIO -- CÓMO FUE MI CONTACTO CON LOS CRUDOS -- FILOSOFÍA PERSONAL SOBRE CRUDIVEGANISMO -- LA RELACIÓN CON OTROS -- DE QUÉ FORMA PUEDES GENERAR INTERÉS EN LA ALIMENTACIÓN CRUDIVEGANA -- ESTABLECER UNA RED SOCIAL DE APOYO -- NECESIDADES NUTRICIONALES DIARIAS/SEMANALES -- LA TOMA DE COMPLEMENTOS DIETÉTICOS -- LA VITAMINA B12 -- LOS MEDICAMENTOS -- ¿POR QUÉ CRUDO? …”

Tabla de Contenidos: “…Sanders -- Claves para generar estrategias de marca ciudad / Guillermo Velasco Barrera -- El estereotipo de género como factor estratégico / Alberto Pedro López-Hermida Russo -- El relato como estrategia política / Miguel Cravioto Sámano -- Hacia un gobierno abierto / Olga Navarro Benavides -- Innovación en el camp de la comunicación política / Rodrigo Solá Villalobos -- Presente y futuro : labor del consultor en las NNTT y las redes sociales / Rafael Rubio -- Inteligencia y encuestas electorales / Cándido Martínez Manrique -- El viaje de las mujeres / Ángela Paloma Martín -- Gestión estratégica de las situaciones de crisis / José Rafael Santana Villegas…”

Tabla de Contenidos: “…Institucionalidad en materia de juventud -- a) Algunas imágenes de los noventa -- b) Los intentos de construir «capital institucional» en juventud -- c) Los actores y sus libretos necesarios para generar «masa crítica» -- 4. Hacia un decálogo de desafíos para una política de juventud -- 5. …”

Tabla de Contenidos: “…Los instrumentos para generar políticas sociales en el caso chileno -- 7. …”

Tabla de Contenidos: “…Algunos apuntes de infancia y primeros años de formación académica -- 1913-1918: un intenso lustro camino de la Cátedra -- Fundamentos de una actitud insurgente -- El más relevante de los catedráticos contestatarios -- República y socialismo en Jiménez de Asúa -- De la promulgación de la Constitución al golpe militar de julio -- París, verano de 1936: en busca del compromiso de las democracias europeas -- La embajada de Praga -- Ginebra: un nuevo frente internacional para Asúa -- Buenos Aires y la universidad para un nuevo comienzo -- Jiménez de Asúa y la política de la Segunda República en el exilio -- Descansar, es comenzar a morir…”

Tabla de Contenidos: “…., Miembros del Coro del Gran Teatro de Ginebra ; Orquesta de la Suisse Romande ; dir., Richard Bonynge ; Los hugonotes : fragmentos / int., Ambrosian Opera Choros ; New Philharmonia Orchestra ; dir., Richard Bonynge…”

Tabla de Contenidos: “…8.5 MBID for Chirp Signal Extraction -- 8.5.1 Chirp‐like Signals -- 8.5.1.1 Linear Chirp -- 8.5.1.2 Frequency‐Shift Key (FSK) Signal -- 8.5.2 Model‐Based Identification: Linear Chirp Signals -- 8.5.2.1 Gauss-Markov State‐Space Model: Linear Chirp -- 8.5.3 Model‐Based Identification: FSK Signals -- 8.5.3.1 Gauss-Markov State‐Space Model: FSK Signals -- 8.5.4 Summary -- References -- Problems -- Appendix A Probability and Statistics Overview -- A.1 Probability Theory -- A.2 Gaussian Random Vectors -- A.3 Uncorrelated Transformation: Gaussian Random Vectors -- A.4 Toeplitz Correlation Matrices -- A.5 Important Processes -- References -- Appendix B Projection Theory -- B.1 Projections: Deterministic Spaces -- B.2 Projections: Random Spaces -- B.3 Projection: Operators -- B.3.1 Orthogonal (Perpendicular) Projections -- B.3.2 Oblique (Parallel) Projections -- References -- Appendix C Matrix Decompositions -- C.1 Singular‐Value Decomposition -- C.2 QR‐Decomposition -- C.3 LQ‐Decomposition -- References -- Appendix D Output‐Only Subspace Identification -- References -- Index -- EULA…”

Tabla de Contenidos: “…Machine generated contents note: Preface List of Abbreviations 1 Introduction 1.1 Forward Problem 1.2 Inverse Problem 1.3 Issues in Inverse Problem Solving 1.4 Linear, Nonlinear and Linearized Problems 2 Signal and System as Vectors 2.1 Vector Space 2.1.1 Vector Space and Subspace 2.1.2 Basis, Norm and Inner Product 2.1.3 Hilbert Space 2.2 Vector Calculus 2.2.1 Gradient 2.2.2 Divergence 2.2.3 Curl 2.2.4 Curve 2.2.5 Curvature 2.3 Taylor's Expansion 2.4 Linear System of Equations 2.4.1 Linear System and Transform 2.4.2 Vector Space of Matrix 2.4.3 Least Square Solution 2.4.4 Singular Value Decomposition (SVD) 2.4.5 Pseudo-inverse 2.5 Fourier Transform 2.5.1 Series Expansion 2.5.2 Fourier Transform 2.5.3 Discrete Fourier Transform (DFT) 2.5.4 Fast Fourier Transform (FFT) 2.5.5 Two-dimensional Fourier Transform References 3 Basics for Forward Problem 3.1 Understanding PDE using Images as Examples 3.2 Heat Equation 3.2.1 Formulation of Heat Equation 3.2.2 One-dimensional Heat Equation 3.2.3 Two-dimensional Heat Equation and Isotropic Diffusion 3.2.4 Boundary Conditions 3.3 Wave Equation 3.4 Laplace and Poisson Equations 3.4.1 Boundary Value Problem 3.4.2 Laplace Equation in a Circle 3.4.3 Laplace Equation in Three-dimensional Domain 3.4.4 Representation Formula for Poisson Equation References 4 Analysis for Inverse Problem 4.1 Examples of Inverse Problems in Medical Imaging 4.1.1 Electrical Property Imaging 4.1.2 Mechanical Property Imaging 4.1.3 Image Restoration 4.2 Basic Analysis 4.2.1 Sobolev Space 4.2.2 Some Important Estimates 4.2.3 Helmholtz Decomposition 4.3 Variational Problems 4.3.1 Lax-Milgram Theorem 4.3.2 Ritz Approach 4.3.3 Euler-Lagrange Equations 4.3.4 Regularity Theory and Asymptotic Analysis 4.4 Tikhonov Regularization and Spectral Analysis 4.4.1 Overview of Tikhonov Regularization 4.4.2 Bounded Linear Operators in Banach Space 4.4.3 Regularization in Hilbert Space or Banach Space 4.5 Basics of Real Analysis 4.5.1 Riemann Integrable 4.5.2 Measure Space 4.5.3 Lebesgue Measurable Function 4.5.4 Pointwise, Uniform, Norm Convergence and Convergence in Measure 4.5.5 Differentiation Theory References 5 Numerical Methods 5.1 Iterative Method for Nonlinear Problem 5.2 Numerical Computation of One-dimensional Heat equation 5.2.1 Explicit Scheme 5.2.2 Implicit Scheme 5.2.3 Crank-Nicolson Method 5.3 Numerical Solution of Linear System of Equations 5.3.1 Direct Method using LU Factorization 5.3.2 Iterative Method using Matrix Splitting 5.3.3 Iterative Method using Steepest Descent Minimization 5.3.4 Conjugate Gradient (CG) Method 5.4 Finite Difference Method (FDM) 5.4.1 Poisson Equation 5.4.2 Elliptic Equation 5.5 Finite Element Method (FEM) 5.5.1 One-dimensional Model 5.5.2 Two-dimensional Model 5.5.3 Numerical Examples References 6 CT, MRI and Image Processing Problems 6.1 X-ray CT 6.1.1 Inverse Problem 6.1.2 Basic Principle and Nonlinear Effects 6.1.3 Inverse Radon Transform 6.1.4 Artifacts in CT 6.2 MRI 6.2.1 Basic Principle 6.2.2 K-space Data 6.2.3 Image Reconstruction 6.3 Image Restoration 6.3.1 Role of p in (6.35) 6.3.2 Total Variation Restoration 6.3.3 Anisotropic Edge-preserving Diffusion 6.3.4 Sparse Sensing 6.4 Segmentation 6.4.1 Active Contour Method 6.4.2 Level Set Method 6.4.3 Motion Tracking for Echocardiography References 7 Electrical Impedance Tomography 7.1 Introduction 7.2 Measurement Method and Data 7.2.1 Conductivity and Resistance 7.2.2 Permittivity and Capacitance 7.2.3 Phasor and Impedance 7.2.4 Admittivity and Trans-impedance 7.2.5 Electrode Contact Impedance 7.2.6 EIT System 7.2.7 Data Collection Protocol and Data Set 7.2.8 Linearity between Current and Voltage 7.3 Representation of Physical Phenomena 7.3.1 Derivation of Elliptic PDE 7.3.2 Elliptic PDE for Four-electrode Method 7.3.3 Elliptic PDE for Two-electrode Method 7.3.4 Min-max Property of Complex Potential 7.4 Forward Problem and Model 7.4.1 Continuous Neumann-to-Dirichlet Data 7.4.2 Discrete Neumann-to-Dirichlet Data 7.4.3 Nonlinearity between Admittivity and Voltage 7.5 Uniqueness Theory and Direct Reconstruction Method 7.5.1 Calderon's Approach 7.5.2 Uniqueness and Three-dimensional Reconstruction: Infinite Measurements 7.5.3 Nachmann's D-bar Method in Two Dimension 7.6 Backprojection Algorithm 7.7 Sensitivity and Sensitivity Matrix 7.7.1 Perturbation and Sensitivity 7.7.2 Sensitivity Matrix 7.7.3 Linearization 7.7.4 Quality of Sensitivity Matrix 7.8 Inverse Problem of EIT 7.8.1 Inverse Problem of RC Circuit 7.8.2 Formulation of EIT Inverse Problem 7.8.3 Ill-posedness of EIT Inverse Problem 7.9 Static Imaging 7.9.1 Iterative Data Fitting Method 7.9.2 Static Imaging using 4-channel EIT System 7.9.3 Regularization 7.9.4 Technical Difficulty of Static Imaging 7.10 Time-difference Imaging 7.10.1 Data Sets for Time-difference Imaging 7.10.2 Equivalent Homogeneous Admittivity 7.10.3 Linear Time-difference Algorithm using Sensitivity Matrix 7.10.4 Interpretation of Time-difference Image 7.11 Frequency-difference Imaging 7.11.1 Data Sets for Frequency-difference Imaging 7.11.2 Simple Difference Ft,ω2− Ft,ω1 7.11.3 Weighted Difference Ft,ω2− [alpha] Ft,ω1 7.11.4 Linear Frequency-difference Algorithm using Sensitivity Matrix 7.11.5 Interpretation of Frequency-difference Image References 8 Anomaly Estimation and Layer Potential Techniques 8.1 Harmonic Analysis and Potential Theory 8.1.1 Layer Potentials and Boundary Value Problems for Laplace Equation 8.1.2 Regularity for Solution of Elliptic Equation along Boundary of Inhomogeneity 8.2 Anomaly Estimation using EIT 8.2.1 Size Estimation Method 8.2.2 Location Search Method 8.3 Anomaly Estimation using Planar Probe 8.3.1 Mathematical Formulation 8.3.2 Representation Formula References 9 Magnetic Resonance Electrical Impedance Tomography 9.1 Data Collection using MRI 9.1.1 Measurement of Bz 9.1.2 Noise in Measured Bz Data 9.1.3 Measurement of B = (Bx,By,Bz) 9.2 Forward Problem and Model Construction 9.2.1 Relation between J , Bz and σ 9.2.2 Three Key Observations 9.2.3 Data Bz Traces σ∇u © e z-directional Change of σ 9.2.4 Mathematical Analysis toward MREIT Model 9.3 Inverse Problem Formulation using B or J 9.4 Inverse Problem Formulation using Bz 9.4.1 Model with Two Linearly Independent Currents 9.4.2 Uniqueness 9.4.3 Defected Bz Data in a Local Region 9.5 Image Reconstruction Algorithm 9.5.1 J-substitution Algorithm 9.5.2 Harmonic Bz Algorithm 9.5.3 Gradient Bz Decomposition and Variational Bz Algorithm 9.5.4 Local Harmonic Bz Algorithm 9.5.5 Sensitivity Matrix Based Algorithm 9.5.6 Anisotropic Conductivity Reconstruction Algorithm 9.5.7 Other Algorithms 9.6 Validation and Interpretation 9.6.1 Image Reconstruction Procedure using Harmonic Bz Algorithm 9.6.2 Conductivity Phantom Imaging 9.6.3 Animal Imaging 9.6.4 Human Imaging 9.7 Applications References 10 Magnetic Resonance Elastography 10.1 Representation of Physical Phenomena 10.1.1 Overview of Hooke's Law 10.1.2 Strain Tensor in Lagrangian Coordinates 10.2 Forward Problem and Model 10.3 Inverse Problem in MRE 10.4 Reconstruction Algorithms 10.4.1 Reconstruction of [mu] with the Assumption of Local Homogeneity 10.4.2 Reconstruction of [mu] without the Assumption of Local Homogeneity 10.4.3 Anisotropic Elastic Moduli Reconstruction 10.5 Technical Issues in MRE References…”

Tabla de Contenidos: “…Motion graphics and beyondcloners; mograph selections; parameters tab; random effector; plain effector; target effector; inheritance effector; shader effector; sound effector; mograph objects and deformers; fracture object; tracer object; mospline object; polyfx; Video Integration; CINEWARE, After Effects, and motion tracking; cineware; multi-pass in cineware; extracting 3D data; camera tracking in after effects; exporting 3D cameras from after effects to c4d; multi-pass to create a shadow catcher in after effects; compositing and external compositing tags; combining imagery with cg…”

Tabla de Contenidos: “…Chapter 4 Determining the Vibration Parameters -- 4.1 Introduction -- 4.2 Amplitude and Frequency Determination -- 4.2.1 Event Detection -- 4.3 Equivalent Linearisation -- 4.4 Hilbert Transform -- 4.5 Time-Varying Linear Approximation -- 4.6 Short Time Fourier Transform -- 4.7 Pinpointing Bifurcations -- 4.7.1 Newton-Raphson -- 4.7.2 Successive Bisection -- 4.8 Limit Cycle Study -- 4.9 Poincaré Sections -- 4.10 Stability of Periodic Solutions -- 4.10.1 Floquet Analysis -- 4.11 Concluding Remarks -- References -- Chapter 5 Bifurcations of FundamentalAeroelastic Systems -- 5.1 Introduction -- 5.2 Two-Dimensional Unsteady Pitch-Plunge-ControlWing -- 5.3 Linear Aeroelastic Analysis -- 5.4 Hardening Stiffness -- 5.4.1 Supercritical Hopf Bifurcation -- 5.4.2 Subcritical Hopf Bifurcation -- 5.4.3 Fold Bifurcation of Cycles -- 5.4.4 Flutter of Nonlinear Systems -- 5.4.5 Period-Doubling Bifurcation -- 5.4.6 Torus Bifurcation -- 5.5 Softening Stiffness -- 5.6 Damping Nonlinearity -- 5.6.1 Subcritical Hopf Bifurcation -- 5.6.2 Static Divergence of Cycles -- 5.6.3 Pitchfork Bifurcation of Cycles -- 5.7 Two-Parameter Bifurcations -- 5.7.1 Generalised Hopf Bifurcation -- 5.7.2 Pitchfork-Hopf Bifurcation -- 5.7.3 Hopf-Hopf Bifurcation -- 5.8 Asymmetric Nonlinear Aeroelastic Systems -- 5.8.1 Fold Bifurcation of Fixed Points and Cycles -- 5.8.2 Transcritical Bifurcation of Fixed Points and Cycles -- 5.8.3 Fold-Hopf Bifurcation -- 5.9 Concluding Remarks -- References -- Chapter 6 Discontinuous Nonlinearities -- 6.1 Introduction -- 6.2 Piecewise Linear Stiffness -- 6.2.1 Underlying and Overlying Linear Systems -- 6.2.2 Fixed Points and Boundary Equilibrium Bifurcations -- 6.2.3 Equivalent Linearisation of Piecewise Linear Stiffness -- 6.2.4 Three-Domain Limit Cycles -- 6.2.5 Two-Domain Limit Cycles -- 6.2.6 Time Domain Solutions…”

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: “…Musical characterisation in HMS Pinafore and the Rocky horror show -- Encoding the voice : Show boat, Guys and dolls, and musical theatre post-1960 -- Integration and distance in musical theatre : the case of Sweeney Todd -- Layers of representation and the creation of irony : Aufstieg und Fall der Stadt Mahagonny, Merrily we roll along and The last 5 years -- Alternatives to linearity : Cabaret, Kiss of the spider woman and Assassins -- Illusions of realism in West Side story and actor-musician performances -- Experiencing live musical theatre performance : La Cage aux Folles and Priscilla, queen of the desert -- I've heard that song before : the jukebox musical and entertainment in Jersey boys, Rock of ages, Mamma mia and We will rock you…”
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