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_
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 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_
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 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_
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":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!