Thereby, if the test was significant, there was a linear dependence between the dependent variable and the independent ones selected.
Univariate logistic analysis yielded relationships of statistical significance between the dependent variable "reporting side effects from current contraceptive use" and 6 independent variables.
Multivariate logistic analysis however produced a statistically significant relationship between the dependent variable and the independent variable "ever told by a health worker about anticipated side effects".
An F-test based on the ratio MSR/MSE can be used to test the statistical significance of the overall relationship between the dependent variable and the set of independent variables.
In multiple regression analysis, the model for simple linear regression is extended to account for the relationship between the dependent variable y and p independent variables x1, x2,..., xp.
F-value was 26.103 indicating significant overall relationship between the dependent variable and the set of all dependent variables.
This matrix (Table 2) shows the correlation between the dependent variable (trip time) and any other independent variable, and also the correlation among the independent variables.
Additionally, a linear regression was conducted between the dependent variable of total woody detritus stocks and the independent climatic variables of TMAX and PRECIP.
The correlations between the dependent variable (PERSISTENCE), representing the maintenance or correction of avoidable qualifications, and the set of independent variables shed some light on our hypotheses.
Non-linear regression is a method for building a non-linear model of the relationship between the dependent variable and a set of independent variables.
The bivariate analysis used frequencies and percentages from cross-tabulations to analyse the association between the dependent variable and socioeconomic characteristics of respondents.
