Simple Linear 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
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
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
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
Correlation and simple regression (Lecture note 5)
Author: MK Garba
School: University of Ilorin
Department: Science and Technology
Course Code: STA204
Topics: Correlation, simple regression, Correlation Analysis, Scatter Plot, Correlation Coefficient
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