Regression Model

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  • The estimation process begins by assuming or hypothesizing that the least squares linear regression model (drawn from a sample) is valid. The formal two-variable linear regression model is based on the following assumptions: (1) The population regression is adequately represented by a straight line: E(Yi) = μ(Xi) = β0 + β1Xi (2) The error terms have zero mean: E(∈i) = 0 (3) A constant variance (homoscedasticity): V(∈i) = σ2

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  • Bài giảng Chapter 3: Stochastic regression model hướng đến trình bày các vấn đề cơ bản như: Consistency; classical stochastic regression model; limiting distributions and asymptotic distributions; asymptotic distribution of;... Mời các bạn cùng tìm hiểu và tham khảo nội dung thông tin tài liệu.

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  • Lecture "Advanced Econometrics (Part II) - Chapter 7: Greneralized linear regression model" presentation of content: Model, properties of ols estimators, white's heteroscedascity consistent estimator, greneralized least squares estimation.

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  • Lecture "Applied econometrics course - Chapter 1: Simple regression model" has content: What is simple regression model, how to estimate simple regression model, R – Square, assumption, variance and standard error of parameters,... and other contents.

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  • Lecture "Applied econometrics course - Chapter 2: Multiple regression model" has content: Why we need multiple regression model, estimation, R – Square, assumption, variance and standard error of parameters, the issues of multiple regression model, Illustration by Computer.

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  • Lecture "Advanced Econometrics (Part II) - Chapter 6: Models for count data" presentation of content: Poisson regression model, goodness of fit, overdispersion, negative binomial regression model, too many zeros data.

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  • Bài giảng Chapter 1: Classical linear regression tập trung trình bày các vấn đề cơ bản về model; assumptions of the classial regression model; least souares estimation;... Mời các bạn cùng tìm hiểu và tham khảo nội dung thông tin tài liệu.

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  • This is a survey of non-linear regression models, with an emphasis on the theory of estimation and hypothesis testing rather than computation and applications, although there will be some discussion of the last two topics. For a general discussion of computation the reader is referred to Chapter 12 of this Handbook by Quandt. My aim is to present the gist of major results; therefore, I will sometimes omit proofs and less significant assumptions. For those, the reader must consult the original sources....

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  • Modeling Hydrologic Change: Statistical Methods is about modeling systems where change has affected data that will be used to calibrate and test models of the systems and where models will be used to forecast system responses after change occurs. The focus is not on the hydrology. Instead, hydrology serves as the discipline from which the applications are drawn to illustrate the principles of modeling and the detection of change. All four elements of the modeling process are discussed: conceptualization, formulation, calibration, and verification.

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  • Regression models form the core of the discipline of econometrics. Although econometricians routinely estimate a wide variety of statistical models, using many different types of data, the vast majority of these are either regression models or close relatives of them. In this chapter, we introduce the concept of a regression model, discuss several varieties of them, and introduce the estimation method that is most commonly used with regression models, namely, least squares.

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography

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  • Tuyển tập các báo cáo nghiên cứu khoa học ngành toán học được đăng trên tạp chí toán học quốc tế đề tài: Two-stage source tracking method using a multiple linear regression model in the expanded phase domain

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Two-stage source tracking method using a multiple linear regression model in the expanded phase domain

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  • Reading is known to be an essential task in language learning, but finding the appropriate text for every learner is far from easy. In this context, automatic procedures can support the teacher’s work. Some tools exist for English, but at present there are none for French as a foreign language (FFL). In this paper, we present an original approach to assessing the readability of FFL texts using NLP techniques and extracts from FFL textbooks as our corpus. Two logistic regression models based on lexical and grammatical features are explored and give quite good predictions on new texts. ...

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  • Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography

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  • Chapter 3 - A brief overview of the classical linear regression model. In this chapter, you will learn how to: Derive the OLS formulae for estimating parameters and their standard errors, explain the desirable properties that a good estimator should have, discuss the factors that affect the sizes of standard errors, test hypotheses using the test of significance and confidence interval approaches, interpret p-values, estimate regression models and test single hypotheses in EViews.

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  • Chapter 4 - Further development and analysis of the classical linear regression model. In this chapter, you will learn how to: Construct models with more than one explanatory variable, test multiple hypotheses using an F-test, determine how well a model fits the data, form a restricted regression, derive the OLS parameter and standard error estimators using matrix algebra, estimate multiple regression models and test multiple hypotheses in EViews.

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  • Chapter 5 - Classical linear regression model assumptions and diagnostics. In this chapter, students will be able to understand: Describe the steps involved in testing regression residuals for heteroscedasticity and autocorrelation, explain the impact of heteroscedasticity or autocorrelation on the optimality of OLS parameter and standard error estimation, distinguish between the Durbin--Watson and Breusch--Godfrey tests for autocorrelation,...

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  • Contents: Single-Equation Regression Models, Relaxing the Assumptions of the Classical Model, Topics in Econometrics, Simultaneous-Equation Models and Time Series Econometrics,...

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