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Polynomial Regression Books

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

Regression and Analysis of Variance I

Author: Alaba Oluwayemisi Oyeronke

School: University of Ibadan

Department: Science and Technology

Course Code: STA322

Topics: Regression, Analysis of Variance, Correlation Coefficient, Correlation Ratio, Simple Linear Regression, Multiple Linear Regression, Multiple Regression Analysis, Polynomial Regression, Non-Linear Regression Model, ANOVA, Randomized Complete Block Design, Analysis of Variance for Randomized Complete Block Design, Latin Square Design, Least Significant Difference

Basic Econometrics ,Fifth Edition

Author: Damodar Gujarati, Dawn Porter

School: Modibbo Adama University of Technology

Department: Administration, Social and Management science

Course Code: CC312

Topics: Single-Equation Regression Models, Regression Analysis.Two-Variable Regression Analysis, Two-Variable Regression Model, Classical Normal Linear Regression Model, Two-Variable Regression, Interval Estimation, Hypothesis Testing, Multiple Regression Analysis, Dummy Variable Regression Models, Multicollinearity, Heteroscedasticity, Autocorrelation, Econometric Modeling, Nonlinear Regression Model, Qualitative Response Regression Models, Panel Data Regression Models, Dynamic Econometric Models, Autoregressive Lag Models, Distributed-Lag Models, Simultaneous-Equation Models, Time Series Econometrics, Simultaneous-Equation Models, Identification Problem, Simultaneous-Equation Methods, Time Series Econometrics, Time Series Econometrics, Forecasting, econometrics

Basic econometrics Student solutions manual for use with Basic econometrics

Author: Damodar Gujarati, Dawn Porter

School: Modibbo Adama University of Technology

Department: Administration, Social and Management science

Course Code: CC312

Topics: econometrics, Single-Equation Regression Models, Regression Analysis.Two-Variable Regression Analysis, Two-Variable Regression Model, Classical Normal Linear Regression Model, Two-Variable Regression, Interval Estimation, Hypothesis Testing, Multiple Regression Analysis, Dummy Variable Regression Models, Multicollinearity, Heteroscedasticity, Autocorrelation, Econometric Modeling, Nonlinear Regression Model, Qualitative Response Regression Models, Panel Data Regression Models, Dynamic Econometric Models, Autoregressive Lag Models, Distributed-Lag Models, Simultaneous-Equation Models, Time Series Econometrics, Simultaneous-Equation Models, Identification Problem, Simultaneous-Equation Methods, Time Series Econometrics, Time Series Econometrics, Forecasting

Applied Numerical Methods with MATLAB, 4th edition

Author: Steven Chapra

School: Edo University

Department: Engineering

Course Code: GEE216

Topics: Numerical Methods, mathematical modeling, MATLAB, mathematical operations, structured programming, errors, roundoff errors, truncation errors, total numerical errors, blunders, model errors, data uncertainty, roots, graphical methods, bracketing methods, bisection, roots, Simple Fixed-Point Iteration, Newton-Raphson, secant methods, Brent's method, MATLAB functions, optimization, linear systems, linear algebraic equations, matrices, Gauss elimination, Naive gauss elimination, tridiagonal systems, LU factorization, matrix inverse, system condition, error analysis, iterative methods, linear systems, nonlinear systems, Eugen values, power method, curve fitting, linear regression, random numbers, linear least-squares regression, polynomial regression, multiple linear regression, QR factorization, nonlinear regression, Fourier analysis, Continuous Fourier series, frequency domain, time domain, Fourier integral, Fourier transform, Discrete Fourier transform, power spectrum, polynomial interpolation, Newton interpolating polynomial, Lagrange interpolating polynomial, inverse interpolation, extrapolation, oscillations, splines, linear splines, quadratic splines, cubic spline, multidimensional interpolation, integration, differentiation, Numerical integration formulas, Newton-Cotes formulas, Trapezoidal rule, Simpson's rules, initial value problem, Runge-Kutta methods, adaptive Runge-Kutta methods, stiff systems, Boundary-value problems, shooting method, finite-difference methods, MATLAB function

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

Introduction To Econometrics

Author: Samuel Olumuyiwa Olusanya

School: National Open University of Nigeria

Department: Administration, Social and Management science

Course Code: ECO355

Topics: Econometrics, Econometrics Model, Linear Regression, Regression Analysis, Ordinary Least Square Method Estimation, Classical Least Regression Method, Ordinary Least Square Estimators, Coefficient of Determination, Classical Normal Linear Regression Model, NORMAL LINEAR REGRESSION MODEL, SINGLE- EQUATION REGRESSION MODELS, ECONOMETRICS ANALYSIS, Method Of Maximum Likelihood, Confidence intervals, Regression Coefficients, Regression Analysis, Analysis of Variance, Normality

Biostatistics for Animal Science, 3rd edition

Author: Miroslav Kaps, William Lamberson

School: National Open University of Nigeria

Department: Agriculture and Veterinary Medicine

Course Code: AGR302

Topics: Biostatistics, measures of central tendency, measures of variability, probability, permutations, random variables, Bernoulli Distribution, Binomial Distribution, Geometric Distribution, Hyper-geometric Distribution, Negative Binomial Distribution, Poisson Distribution, Multinomial Distribution, Uniform Distribution, Normal Distribution, Multivariate Normal Distribution, Logistic Distribution, Chi-square Distribution, Gamma Distribution, Exponential Distribution, Beta Distribution, Dirichlet Distribution, population sample, central limit theorem, degrees of freedom, parameters estimation, point estimation, maximum likelihood estimation, interval estimation, hypothesis testing, simple linear regression, simple regression model, partitioning total variability, partial correlation, rank correlation, likelihood ratio test, robust regression, curvilinear regression, polynomial regression, segmented regression, Least Significance Difference, intraclass correlation, experimental error, randomized complete block design, Partitioning Total Variability

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