Panel Data Regression Models 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
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
Author: Thomas Connolly, Carolyn Begg
School: Edo University
Department: Science and Technology
Course Code: CMP222, CMP214
Topics: Database Systems, database environment, database languages, data definition language, data models conceptual modeling, database architectures, Multi-user DBMS Architectures, teleprocessing, Distributed DBMSs, data warehousing, cloud computing, Oracle Architecture, relational models, relational calculus, SQL, writing SQL commands, data manipulation, advanced SQL, object-relational DBMs, Database System Development Lifecycle, database analysis, database design, database planning, Entity–Relationship Modeling, Enhanced Entity–Relationship Modeling, normalization, Data Redundancy, Advanced Normalization, database security, data administration, database administration, concurrency control, database recovery, query processing, query optimization, distributed DBMs, Distributed Transaction Management, Distributed Concurrency Control, Distributed Deadlock Management, Distributed Database Recovery, data replication, data Replication Architecture, data Replication Schemes, object oriented DBMs, Scripting Languages, web, Common Gateway Interface, HTTP cookies, Microsoft’s Web Platform, JAVA, Oracle Internet Platform, Semi structured Data, XML, XML schema, XML Query Languages, Data Warehouse Architecture, Data Warehousing Tools, Data Warehousing Technologies, Data Warehousing Using Oracle, data mart, Data Warehousing Design, Online Analytical Processing, data mining, oracle data mining
Applied Econometrics ,2nd edition
Author: Dimitrios Asteriou, Stephen Hall
School: National Open University of Nigeria
Department: Administration, Social and Management science
Course Code: ECO355
Topics: Applied Econometrics, Econometrics, Economic Data, Basic Data Handling, Simple Regression, Classical Linear Regression Model, Multiple Regression, Multicollinearity, Heteroskedasticity, Autocorrelation, Misspecification, Wrong Regressors, Measurement Errors, Wrong Functional Forms, Dummy Variables, Dynamic Econometric Models, Simultaneous Equation Models, Limited Dependent Variable Regression Models, Time Series Econometrics, ARIMA Models, Box–Jenkins Methodology, ARCH model, GARCH model, Vector Autoregressive Models, Causality Tests, Non-Stationarity Tests, Unit-Root Tests, Cointegration, Error-Correction Models, Solving Models, Panel Data Econometrics, Panel Data Models, Dynamic Heterogeneous Panels, Non-Stationary Panels, Econometric Software
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
Econometric Analysis ,8th edition
Author: William Greene
School: National Open University of Nigeria
Department: Administration, Social and Management science
Course Code: ECO454
Topics: Linear Regression Model, Econometrics, Least Squares Regression, Hypothesis Tests, Model Selection, Functional Form, Difference in Differences, Structural Change, Nonlinear Regression Models, Semiparametric Regression Models, Nonparametric Regression Models, Endogeneity, Instrumental Variable Estimation, Generalized Regression Model, Heteroscedasticity, Regression Equations, Estimation Frameworks, Estimation Methodology, Minimum Distance Estimation, Generalized Method of Moments, Maximum Likelihood Estimation, Simulation-Based Estimation, Inference, Random Parameter Models, Bayesian Estimation, Cross Sections, Panel Data, Microeconometrics, Binary Outcomes, Discrete Choices, Multinomial Choices, Event Counts, Limited Dependent Variables—Truncation, Censoring, Sample Selection, Time Series, Macroeconometrics, Serial Correlation, Nonstationary Data
An introduction to generalized linear models ,4th edition
Author: Annette Dobson, Adrian Barnett
School: University of Ibadan
Department: Science and Technology
Course Code: STA351
Topics: generalized linear models, Model Fitting, Exponential Family, estimation, inference, normal linear models, Binary Variables, Logistic Regression, Nominal Logistic Regression, Ordinal Logistic Regression, Poisson Regression, Log-Linear Models, Survival Analysis, Clustered data, Longitudinal Data, Bayesian Analysis, Markov Chain Monte Carlo Methods
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