Statistics



Statistics

Introduction

The different variables taken in this analysis are Family, Cultural Identity, Community support, Self Esteem as independent variables and depression being the independent variable. The data has been marked through the likert - scale i.e. depression has been marked from 1 to 10(1 being not depressed and 10 being highly depressed). Similarly, the variable self esteem has also been marked from 1 to 10(1 being not depressed and 10 being highly depressed (Robert, 2003).

The family has been marked as residents and non - residents (i.e. 1 and 2 respectively). The variable of community support has been taken in the range from 1 to 5 (i.e. from no community help to complete community help respectively).

Findings

The bivariate analysis for the model shows that the R - square for the model is 0.894 which shows great dependency of the independent variables on the dependent variable. The empirical results show that the independent variables are 89.4% dependent on depression (Berg, 2007). The adjusted R - square is also high showing that all the variables are significant in the model and that any exclusion of variables from the model will make significant changes in the reliability of the model. This R - square shows the overall model efficiency that the overall model is well structured and defines the dependent variable efficiently.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

. 841a

.894

.752

2.642

a. Predictors: (Constant), Family, Cultural Identity, Community support, Self Esteem

The coefficients of the variables are an extremely important part of the model which shows that whether the variables are significant or not. The coefficients in this model shows that they are almost all significant because the t - value is highly significant as it is greater than the tabulated values and also the significance value is less than 0.05 (Alan, 2004). The signs of ...