Results 211 to 220 of about 1,076,604 (262)
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2013
The correlation coefficient discussed in the last chapter is a component of one of the most important techniques in statistics: linear regression modeling. In this section, we introduce this topic and the subject of statistical modeling, in general.
Alfred DeMaris, Steven H. Selman
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The correlation coefficient discussed in the last chapter is a component of one of the most important techniques in statistics: linear regression modeling. In this section, we introduce this topic and the subject of statistical modeling, in general.
Alfred DeMaris, Steven H. Selman
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2009
The main focus of this chapter will be the linear regression model and its basic principle of estimation.We introduce the fundamental method of least squares by looking at the least squares geometry and discussing some of its algebraic properties.
Helge Toutenburg, null Shalabh
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The main focus of this chapter will be the linear regression model and its basic principle of estimation.We introduce the fundamental method of least squares by looking at the least squares geometry and discussing some of its algebraic properties.
Helge Toutenburg, null Shalabh
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2003
In this chapter, we consider point estimation of the parameters s ∈ ℝ P and σ2 ∈ (0, ∞) in the linear regression model $$y = X\beta + \varepsilon , \varepsilon \sim (0,{{\sigma }^{2}}{{I}_{n}}) $$ We will focus our attention to the ordinary least squares estimator $$ \hat \beta = (X'X)^{ - 1} X'y $$ and the least squares variance estimator
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In this chapter, we consider point estimation of the parameters s ∈ ℝ P and σ2 ∈ (0, ∞) in the linear regression model $$y = X\beta + \varepsilon , \varepsilon \sim (0,{{\sigma }^{2}}{{I}_{n}}) $$ We will focus our attention to the ordinary least squares estimator $$ \hat \beta = (X'X)^{ - 1} X'y $$ and the least squares variance estimator
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2011
A model is nonlinear if any of the partial derivatives with respect to any of the model parameters are dependent on any other model parameter or if any of the derivatives do not exist or are discontinuous. This chapter expands on the previous chapter and introduces nonlinear regression within a least squares (NLS) and maximum likelihood framework.
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A model is nonlinear if any of the partial derivatives with respect to any of the model parameters are dependent on any other model parameter or if any of the derivatives do not exist or are discontinuous. This chapter expands on the previous chapter and introduces nonlinear regression within a least squares (NLS) and maximum likelihood framework.
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Applied Linear Regression Models
Technometrics, 1985F. H. C. Marriott +3 more
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1993
Although this book deals with nonlinear models, a short chapter on linear regression models may be useful, since by comparison with the linear case one can better understand some features of nonlinear models. Moreover, linear regression models are probably the most popular models in applications.
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Although this book deals with nonlinear models, a short chapter on linear regression models may be useful, since by comparison with the linear case one can better understand some features of nonlinear models. Moreover, linear regression models are probably the most popular models in applications.
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