#+TITLE: Rapport package team
#+AUTHOR: GLM
#+DATE: 2011-04-26 20:25 CET
** Description
In this template Rapporter will present you GLM.
*** Introduction
[[http://en.wikipedia.org/wiki/Generalized_linear_model][Generalized
Linear Model (GLM)]] is a generalization of the ordinary
[[http://en.wikipedia.org/wiki/Linear_regression][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
[[https://en.wikipedia.org/wiki/Hierarchical_generalized_linear_model][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
[[http://en.wikipedia.org/wiki/Interaction][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 |
#+CAPTION: 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
#+BEGIN_HTML
#+END_HTML
** Description
In this template Rapporter will present you GLM.
*** Introduction
[[http://en.wikipedia.org/wiki/Generalized_linear_model][Generalized
Linear Model (GLM)]] is a generalization of the ordinary
[[http://en.wikipedia.org/wiki/Linear_regression][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
[[https://en.wikipedia.org/wiki/Hierarchical_generalized_linear_model][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
[[http://en.wikipedia.org/wiki/Interaction][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 |
#+CAPTION: 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
#+BEGIN_HTML
#+END_HTML
** Description
In this template Rapporter will present you GLM.
*** Introduction
[[http://en.wikipedia.org/wiki/Generalized_linear_model][Generalized
Linear Model (GLM)]] is a generalization of the ordinary
[[http://en.wikipedia.org/wiki/Linear_regression][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
[[https://en.wikipedia.org/wiki/Hierarchical_generalized_linear_model][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
[[http://en.wikipedia.org/wiki/Interaction][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 |
#+CAPTION: 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
#+BEGIN_HTML
#+END_HTML
--------------
This report was generated with [[http://www.r-project.org/][R]] (3.0.1)
and [[http://rapport-package.info/][rapport]] (0.51) in /0.681/ sec on
x86\_64-unknown-linux-gnu platform.
[[images/logo.png]]