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Forecasting Components
Forecasting Components

... 1. Times Series - Statistical techniques that use historical data to predict future behavior. 2. Regression Methods - Regression (or causal ) methods that attempt to develop a mathematical relationship between the item being forecast and factors that cause it to behave the way it does. 3. Qualitativ ...
The SURVEYLOGISTIC Procedure
The SURVEYLOGISTIC Procedure

Applied Generalized Linear Mixed Models: Continuous and Discrete
Applied Generalized Linear Mixed Models: Continuous and Discrete

2015, Gittner, Lisa S., Barbara Kilbourne, Katy Kilbourne, and Yongwan Chun , Climate Change and the Rise of Obesity, Jacobs Journal of Environmental Sciences , 1(2):009.
2015, Gittner, Lisa S., Barbara Kilbourne, Katy Kilbourne, and Yongwan Chun , Climate Change and the Rise of Obesity, Jacobs Journal of Environmental Sciences , 1(2):009.

The LOGISTIC Procedure
The LOGISTIC Procedure

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Threshold detection: matching statistical methodology to ecological

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Statistical Methods for Genetic Association Studies under the
Statistical Methods for Genetic Association Studies under the

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Model Uncertainty in Panel Vector Autoregressive Models
Model Uncertainty in Panel Vector Autoregressive Models

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Impartial Predictive Modeling: Ensuring Fairness
Impartial Predictive Modeling: Ensuring Fairness

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The GEE Procedure
The GEE Procedure

... Fitzmaurice, Laird, and Ware 2011; Diggle et al. 2002). For longitudinal studies, missing data are common, and they can be caused by dropouts or skipped visits. If missing responses depend on previous responses, the usual GEE approach can lead to biased estimates. So the GEE procedure also implement ...
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Cogito ergo sum” …or do I?: Causality vs Statistical

... Fig. 1. This is a schematic illustrating the concepts underlying dynamic causal modelling. In particular it highlights the two distinct ways in which inputs or perturbations can elicit responses in the regions or nodes that compose the model. In this example there are five nodes, including visual ar ...
Adaptive Testing on a Regression Function at a Point Yale University
Adaptive Testing on a Regression Function at a Point Yale University

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Empirical Research on the Earnings Manipulation Behavior of the
Empirical Research on the Earnings Manipulation Behavior of the

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... automation effect. In this paper I apply a logistic regression analysis to get more information about the combined centralization and automation effect. I was also interested in gaining understanding of the performance of this technique with CPS and related data. In that context, this paper should b ...
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Regression analysis

In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution.Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. However this can lead to illusions or false relationships, so caution is advisable; for example, correlation does not imply causation.Many techniques for carrying out regression analysis have been developed. Familiar methods such as linear regression and ordinary least squares regression are parametric, in that the regression function is defined in terms of a finite number of unknown parameters that are estimated from the data. Nonparametric regression refers to techniques that allow the regression function to lie in a specified set of functions, which may be infinite-dimensional.The performance of regression analysis methods in practice depends on the form of the data generating process, and how it relates to the regression approach being used. Since the true form of the data-generating process is generally not known, regression analysis often depends to some extent on making assumptions about this process. These assumptions are sometimes testable if a sufficient quantity of data is available. Regression models for prediction are often useful even when the assumptions are moderately violated, although they may not perform optimally. However, in many applications, especially with small effects or questions of causality based on observational data, regression methods can give misleading results.In a narrower sense, regression may refer specifically to the estimation of continuous response variables, as opposed to the discrete response variables used in classification. The case of a continuous output variable may be more specifically referred to as metric regression to distinguish it from related problems.
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