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+Regression modeling deals with the description of the sampling distribution of a given random variable \( y \) varies as function of another variable or a set of such variables \( \hat{x} =[x_0, x_1,\dots, x_p] \). +The first variable is called the dependent, the outcome or the response variable while the set of variables \( \hat{x} \) is called the independent variable, or the predictor variable or the explanatory variable. + +
+A regression model aims at finding a likelihood function \( p(y\vert \hat{x}) \), that is the conditional distribution for \( y \) with a given \( \hat{x} \). The estimation of \( p(y\vert \hat{x}) \) is made using a data set with + +
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+Regression modeling deals with the description of the sampling distribution of a given random variable \( y \) varies as function of another variable or a set of such variables \( \hat{x} =[x_0, x_1,\dots, x_p] \).
+The first variable is called the dependent, the outcome or the response variable while the set of variables \( \hat{x} \) is called the independent variable, or the predictor variable or the explanatory variable.
+
+
+A regression model aims at finding a likelihood function \( p(y\vert \hat{x}) \), that is the conditional distribution for \( y \) with a given \( \hat{x} \). The estimation of \( p(y\vert \hat{x}) \) is made using a data set with
+
+
+
+ The goal of the regression analysis is to extract/exploit relationship between \( y_i \) and \( \hat{x}_i \) in or to infer causal dependencies, approximations to the likelihood functions, functional relationships and to make predictions .
+
+more text to come
+
+
Introduction
+Regression analysis, overarching aims
+
+General linear models
+
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+Regression modeling deals with the description of the sampling distribution of a given random variable \( y \) varies as function of another variable or a set of such variables \( \hat{x} =[x_0, x_1,\dots, x_p] \). +The first variable is called the dependent, the outcome or the response variable while the set of variables \( \hat{x} \) is called the independent variable, or the predictor variable or the explanatory variable. + +
+A regression model aims at finding a likelihood function \( p(y\vert \hat{x}) \), that is the conditional distribution for \( y \) with a given \( \hat{x} \). The estimation of \( p(y\vert \hat{x}) \) is made using a data set with + +
+
+
+
+more text to come +