h2(#description). Description
In this template Rapporter will present you GLM.
h3(#introduction). 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.
h1(#overview). 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.
Fitting General Linear Model: age based on _leisure_ and _edu_
| *(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 |
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
h2(#description-1). Description
In this template Rapporter will present you GLM.
h3(#introduction-1). 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.
h1(#overview-1). 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.
Fitting General Linear Model: age based on _leisure_ and _edu_
| *(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 |
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
h2(#description-2). Description
In this template Rapporter will present you GLM.
h3(#introduction-2). 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.
h1(#overview-2). 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.
Fitting General Linear Model: age based on _leisure_ and _edu_
| *(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 |
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.
!images/logo.png!