10 Dichotomous or binary responses
... skip it (sections 10.11 through 10.13) on first reading because it is not essential for understanding and interpreting the models. Other approaches to clustered data with binary responses, such as fixed-intercept models (conditional maximum likelihood) and generalized estimating equations (GEE) are ...
... skip it (sections 10.11 through 10.13) on first reading because it is not essential for understanding and interpreting the models. Other approaches to clustered data with binary responses, such as fixed-intercept models (conditional maximum likelihood) and generalized estimating equations (GEE) are ...
3sls inst(t wg g yr p1 x1 k1)
... equations, where some equations contain endogenous variables among the explanatory variables. Estimation is via three-stage least squares (3SLS). Typically, the endogenous regressors are dependent variables from other equations in the system. In addition, reg3 can also estimate systems of equations ...
... equations, where some equations contain endogenous variables among the explanatory variables. Estimation is via three-stage least squares (3SLS). Typically, the endogenous regressors are dependent variables from other equations in the system. In addition, reg3 can also estimate systems of equations ...
Discrete-Time Methods for the Analysis of Event Histories Author(s
... rests during the one-year period. An ad hoc solution to this dilemma might be to exclude all censored observations and just look at those cases for whom an arrest is observed. But the number of censored cases may be large (47 percent were censored in this sample), and it has been shown that their ex ...
... rests during the one-year period. An ad hoc solution to this dilemma might be to exclude all censored observations and just look at those cases for whom an arrest is observed. But the number of censored cases may be large (47 percent were censored in this sample), and it has been shown that their ex ...
Supervised learning (3)
... method is also robust to redundant and nonlinear data. • The algorithm is easy to use, and the output (the tree) is relatively easy to understand. • Once the model is fit, scoring is fast. ...
... method is also robust to redundant and nonlinear data. • The algorithm is easy to use, and the output (the tree) is relatively easy to understand. • Once the model is fit, scoring is fast. ...
non-linear latent variable models: a study of factor score
... corresponding to the effect of the latent product vector ξe on the indicator product vector x lastly, δe is the error vector of the product equations. For example, the measurement model for equations 1, 2 and 4 is illustrated in figure 2. The indicator variables associated with the new factor ξ̃ = ξ ...
... corresponding to the effect of the latent product vector ξe on the indicator product vector x lastly, δe is the error vector of the product equations. For example, the measurement model for equations 1, 2 and 4 is illustrated in figure 2. The indicator variables associated with the new factor ξ̃ = ξ ...
Part I Simple Linear Regression - the Department of Statistics Online
... Part I Simple Linear Regression ...
... Part I Simple Linear Regression ...
On the Degree of Openness of an Open Economy
... technologies and constant returns, relative costs can differ if factor proportions differ. Other authors have focused on economies of scale as another determining factor of comparative advantage. A related concept that has received much less attention from theorists is that of the degree of openness ...
... technologies and constant returns, relative costs can differ if factor proportions differ. Other authors have focused on economies of scale as another determining factor of comparative advantage. A related concept that has received much less attention from theorists is that of the degree of openness ...
Linear regression
In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. (This term should be distinguished from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.)In linear regression, data are modeled using linear predictor functions, and unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X. Less commonly, linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X, which is the domain of multivariate analysis.Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications. This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.Linear regression has many practical uses. Most applications fall into one of the following two broad categories: If the goal is prediction, or forecasting, or error reduction, linear regression can be used to fit a predictive model to an observed data set of y and X values. After developing such a model, if an additional value of X is then given without its accompanying value of y, the fitted model can be used to make a prediction of the value of y. Given a variable y and a number of variables X1, ..., Xp that may be related to y, linear regression analysis can be applied to quantify the strength of the relationship between y and the Xj, to assess which Xj may have no relationship with y at all, and to identify which subsets of the Xj contain redundant information about y.Linear regression models are often fitted using the least squares approach, but they may also be fitted in other ways, such as by minimizing the ""lack of fit"" in some other norm (as with least absolute deviations regression), or by minimizing a penalized version of the least squares loss function as in ridge regression (L2-norm penalty) and lasso (L1-norm penalty). Conversely, the least squares approach can be used to fit models that are not linear models. Thus, although the terms ""least squares"" and ""linear model"" are closely linked, they are not synonymous.