% Rapport package team
% GLM
% 2011-04-26 20:25 CET
## Description
In this template Rapporter will present you GLM.
### Introduction
[Generalized Linear Model (GLM)](http://en.wikipedia.org/wiki/Generalized_linear_model) is a generalization of the ordinary [Linear Regression](http://en.wikipedia.org/wiki/Linear_regression). While using GLM we don't need the assumption of normality for response variables. There are two basic ideas of the model: It allows the linear model to be related to the response variable via a link function and the magnitude of the variance of each measurement to be a function of its predicted value. An extinsion to the GLM is the [Hierarchical generalized linear model](https://en.wikipedia.org/wiki/Hierarchical_generalized_linear_model).
#Overview
Multivariate-General Linear Model was carried out, with _Internet usage in leisure time (hours per day)_ and _Internet usage for educational purposes (hours per day)_ as independent variables, and _Age_ as a dependent variable.
The [interaction](http://en.wikipedia.org/wiki/Interaction) between the independent variables was taken into account.
--------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
----------------- ---------- ------------ --------- ----------
**(Intercept)** 3.198 0.02122 150.7 0
**leisure** -0.02021 0.005847 -3.457 0.000547
**edu** 0.01474 0.007586 1.944 0.05196
**leisure:edu** 0.004439 0.001795 2.472 0.01342
--------------------------------------------------------------
Table: Fitting General Linear Model: age based on _leisure_ and _edu_
From the table one can see that
* (Intercept) has significant effect on the dependent variable, the p-value of that is 0
* leisure has significant effect on the dependent variable, the p-value of that is 0.001
* leisure:edu has significant effect on the dependent variable, the p-value of that is 0.013
## Description
In this template Rapporter will present you GLM.
### Introduction
[Generalized Linear Model (GLM)](http://en.wikipedia.org/wiki/Generalized_linear_model) is a generalization of the ordinary [Linear Regression](http://en.wikipedia.org/wiki/Linear_regression). While using GLM we don't need the assumption of normality for response variables. There are two basic ideas of the model: It allows the linear model to be related to the response variable via a link function and the magnitude of the variance of each measurement to be a function of its predicted value. An extinsion to the GLM is the [Hierarchical generalized linear model](https://en.wikipedia.org/wiki/Hierarchical_generalized_linear_model).
#Overview
Multivariate-General Linear Model was carried out, with _Internet usage in leisure time (hours per day)_ and _Internet usage for educational purposes (hours per day)_ as independent variables, and _Age_ as a dependent variable.
The [interaction](http://en.wikipedia.org/wiki/Interaction) between the independent variables wasn't taken into account.
--------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
----------------- ---------- ------------ --------- ----------
**(Intercept)** 3.163 0.01605 197.1 0
**leisure** -0.0095 0.003888 -2.443 0.01455
**edu** 0.03071 0.003883 7.91 2.581e-15
--------------------------------------------------------------
Table: Fitting General Linear Model: age based on _leisure_ and _edu_
From the table one can see that
* (Intercept) has significant effect on the dependent variable, the p-value of that is 0
* leisure has significant effect on the dependent variable, the p-value of that is 0.015
* edu has significant effect on the dependent variable, the p-value of that is 0
## Description
In this template Rapporter will present you GLM.
### Introduction
[Generalized Linear Model (GLM)](http://en.wikipedia.org/wiki/Generalized_linear_model) is a generalization of the ordinary [Linear Regression](http://en.wikipedia.org/wiki/Linear_regression). While using GLM we don't need the assumption of normality for response variables. There are two basic ideas of the model: It allows the linear model to be related to the response variable via a link function and the magnitude of the variance of each measurement to be a function of its predicted value. An extinsion to the GLM is the [Hierarchical generalized linear model](https://en.wikipedia.org/wiki/Hierarchical_generalized_linear_model).
#Overview
Multivariate-General Linear Model was carried out, with _Internet usage in leisure time (hours per day)_ and _Internet usage for educational purposes (hours per day)_ as independent variables, and _Age_ as a dependent variable.
The [interaction](http://en.wikipedia.org/wiki/Interaction) between the independent variables wasn't taken into account.
--------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
----------------- ---------- ------------ --------- ----------
**(Intercept)** 0.0422 0.0008599 49.08 4.612e-212
**leisure** 0.0003828 0.0002093 1.829 0.06785
**edu** -0.001182 0.0001948 -6.065 2.332e-09
--------------------------------------------------------------
Table: Fitting General Linear Model: age based on _leisure_ and _edu_
From the table one can see that
* (Intercept) has significant effect on the dependent variable, the p-value of that is 0
* edu has significant effect on the dependent variable, the p-value of that is 0
-------
This report was generated with [R](http://www.r-project.org/) (3.0.1) and [rapport](https://rapporter.github.io/rapport/) (0.51) in _0.681_ sec on x86_64-unknown-linux-gnu platform.
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