Stanford Linear Accelerator Books
Principles of Soil Chemistry, 4th Edition
Author: Kim Tan
School: National Open University of Nigeria
Department: Agriculture and Veterinary Medicine
Course Code: SLM301
Topics: Soil Chemistry, analytical chemistry, geochemistry, Particle Accelerators, Synchrotrons, Stanford Linear Accelerator, Continuous Electron Beam Accelerator, Fermi National Laboratory Accelerator, Brookhaven Realistic Heavy Ion Collider, quarks, leptons, Atomic Numbers, Avogadro’s Number, atomic orbitals, Acid–Base Titrations, Precipitation, Complex Reactions, Chemical Units, Molarity, Molality, Radioactivity, isotopes, mole fractions, Soil Composition, Electrochemical Potentials, carbon dating, Electrochemical Cells, Nernst Equation, Electron Activity, chemical potential, membrane potential, Soil Gas, Oxygen Revolution, soil aeration, soil aerification, Soil Air Quality, Hydrotropism, Hypoxia, soil water chemistry, Oxygen Demand of Water, Total Soil Water Potential, Matric Potential, Pressure Potential, Osmotic Potential, Gravitational Potential, Plant–Soil–Water Energy Relation, water dissociation, Colloidal Chemistry, Colloidal System, soil humus, Carbohydrates, Amino Acids, Peptides, Proteins, Nucleic Acids, lipids, Lignins, humic matter, Electron Microscopy, Clay Minerals, Surface Potential, Electric Double Layer, Helmholtz Double-Layer Theory, Gouy–Chapman Double-Layer Theory, Adsorption Isotherms, soil adsorption, water adsorption, Cation Exchange, Cation Exchange Reactions, Cation Exchange Capacity, Langmuir–Vageler Equation, Mono-Divalent Cation Exchange Reaction, Mono-Monovalent Cation Exchange Reaction, Anion Exchange, Phosphate Retention, Phosphate fixation, Phosphate Potential, Soil Reaction, Acid–Base Chemistry, Weathering, Coordination Theory, Complex Formation, chelation, Metal–Organic Complex Reactions, Clay–Organic Compound Complexes
Linear And Nonlinear Programming, 4th Edition
Author: David Luenberger, Yinyu Ye
School: Federal University of Technology, Owerri
Department: Engineering
Course Code: ENG308
Topics: Linear programming, simplex method, linear programs, Duality, Complementarity, interior-point methods, conic linear programming, unconstrained problems, concave functions, convex functions, speed of convergence, quasi-newton methods, constrained minimization, penalty method, barrier method, duality method, dual method, primal-dual method
A First Course in Linear Algebra
Author: Robert Beezer
School: Edo University
Department: Science and Technology
Course Code: MTH214
Topics: Linear algebra, vector, Reduced Row-Echelon Form, vector operations, linear combinations, spanning sets, linear independence, orthogonality, matrices, matrix operation, matrix multiplication, matrix inverses, vector spaces, subspaces, matrix determinants, Eigenvalues, Eigen vectors, linear transformations, Injective Linear Transformations, Surjective Linear Transformations, Invertible Linear Transformations, vector representations, matrix representations, complex number operations, sets
Author: MAT212
School: University of Ibadan
Department: Science and Technology
Course Code: MAT212
Topics: Linear Algebra, Algebra of Matrices, matrix, Determinants, Matrix Inverse, Systems of Linear Equations, Vector Space, linear equation, Subspaces of Vector Spaces, Rank of a Matrix, Linear Transformations, Linear Transformation, Homogeneous Systems of Linear Equations, Non-Homogeneous Systems of Linear Equations, Eigenvalue, Eigenvector, Minimal Polynomial, Matrix Polynomial, Companion Matrix, Similar Matrix, Diagonal Matrix, Triangular Matrix
Introduction to Linear Regression Analysis ,5th edition
Author: Elizabeth Peck, Geoffrey Vining, Douglas Montgomery
School: University of Ibadan
Department: Science and Technology
Course Code: STA351
Topics: Linear Regression Analysis, Regression, Model Building, Data Collection, Simple Linear Regression Model, Simple Linear Regression, Least-Squares Estimation, Hypothesis Testing, Interval Estimation, Multiple Regression Models, Multiple linear regression, Hypothesis Testing, Confidence Intervals, Standardized Regression Coefficients, Multicollinearity, Residual Analysis, model adequacy checking, Variance-Stabilizing Transformations, Generalized Least Squares, Weighted Least Squares, Regression Models, subsampling, Leverage, Measures of Influence, influence, Polynomial regression Models, Piecewise Polynomial Fitting, Nonparametric Regression, Kernel Regression, Locally Weighted Regression, Orthogonal Polynomials, Indicator Variables, Multicollinearity, Multicollinearity Diagnostics, Model-Building, regression models, Linear Regression Models, Nonlinear Regression Models, Nonlinear Least Squares, Logistic Regression Models, Poisson regression, Time Series Data, Detecting Autocorrelation, Durbin-Watson Test, Time Series Regression, Robust Regression, Inverse Estimation
Introduction to Linear Regression Analysis Solutions Manual for 5th edition
Author: Ann Ryan, Douglas Montgomery, Elizabeth Peck, Geoffrey Vining
School: University of Ibadan
Department: Science and Technology
Course Code: STA351
Topics: Linear Regression Analysis, Regression, Model Building, Data Collection, Simple Linear Regression Model, Simple Linear Regression, Least-Squares Estimation, Hypothesis Testing, Interval Estimation, Multiple Regression Models, Multiple linear regression, Hypothesis Testing, Confidence Intervals, Standardized Regression Coefficients, Multicollinearity, Residual Analysis, model adequacy checking, Variance-Stabilizing Transformations, Generalized Least Squares, Weighted Least Squares, Regression Models, subsampling, Leverage, Measures of Influence, influence, Polynomial regression Models, Piecewise Polynomial Fitting, Nonparametric Regression, Kernel Regression, Locally Weighted Regression, Orthogonal Polynomials, Indicator Variables, Multicollinearity, Multicollinearity Diagnostics, Model-Building, regression models, Linear Regression Models, Nonlinear Regression Models, Nonlinear Least Squares, Logistic Regression Models, Poisson regression, Time Series Data, Detecting Autocorrelation, Durbin-Watson Test, Time Series Regression, Robust Regression, Inverse Estimation
Schaums Outline of Linear Algebra, 6th Edition
Author: Seymour Lipschutz, Marc Lipson
School: Edo University
Department: Science and Technology
Course Code: MTH214
Topics: Linear Algebra, Matrix algebra, matrix multiplication, Equivalent Systems, Elementary Operations, Gaussian Elimination, Echelon Matrices, Row Canonical Form, Row Equivalence, Matrix Formulation, Elementary Matrices, LU Decomposition, vector spaces, Linear Combinations, spanning sets, Full Rank Factorization, Least Square Solution, linear mappings, Cauchy–Schwarz Inequality, Gram–Schmidt Orthogonalization, determinants, diagonalization, Eigenvalues, Eigenvectors, Cayley–Hamilton Theorem, canonical forms, linear functionals, dual space, bilinear form, quadratic forms, Hermitian Form, linear operators
Applied Linear Statistical Models 5th Edition Instructors Solutions Manual
Author: Michael Kutner, Christopher Nachtsheim, John Neter, William Li
School: University of Ibadan
Department: Science and Technology
Course Code: STA322
Topics: Linear Statistical Models, linear regression, inference, correlation analysis, simultaneous inferences, regression analysis, simple linear regression analysis, multiple regression, quantitative predictors, qualitative predictors, regression, model, autocorrelation, time series, nonlinear regression, Neural networks, Logistic regression, Possion regression, Generalized linear models, ANOVA, Two-factor analysis of variance, two-factor studies, randomized complete block designs, analysis of covariance, multifactor studies, Nested designs, subsampling, partially nested designs
Applied Linear Statistical Models,5th edition
Author: Michael Kutner, Christopher Nachtsheim, John Neter, William Li
School: University of Ibadan
Department: Science and Technology
Course Code: STA322
Topics: Linear Statistical Models, linear regression, inference, correlation analysis, simultaneous inferences, regression analysis, simple linear regression analysis, multiple regression, quantitative predictors, qualitative predictors, regression, model, autocorrelation, time series, nonlinear regression, Neural networks, Logistic regression, Possion regression, Generalized linear models, ANOVA, Two-factor analysis of variance, two-factor studies, randomized complete block designs, analysis of covariance, multifactor studies, Nested designs, subsampling, partially nested designs
Author: Jörg Liesen, Volker Mehrmann
School: University of Ilorin
Department: Science and Technology
Course Code: MAT206, MAT213, PHY464, ELE576
Topics: algebraic structures, matrix, echelon form, Gaussian elimination, linear system, vector space, linear map, linear form, bilinear form, Euclidean vector space, unitary vector space, eigenvalue, endomorphism, polynomials, theory of algebra, cyclic subspace, duality, Jordan canonical form, matrix function, singular value decomposition, Kronecker product, linear matrix
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