In this template Rapporter will present you GLM.

Generalized Linear Model (GLM) is a generalization of the ordinary 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.

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 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 |

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

In this template Rapporter will present you GLM.

Generalized Linear Model (GLM) is a generalization of the ordinary 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.

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 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 |

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

In this template Rapporter will present you GLM.

Generalized Linear Model (GLM) is a generalization of the ordinary 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.

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 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 |

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 (3.0.1) and rapport (0.51) in *0.681* sec on x86_64-unknown-linux-gnu platform.