The independent variable is the Expected years of schooling for females and the dependent variable is the female unemployment rate. The dependent variable is in the y-axis and the independent variable in the x-axis.
The null hypothesis
H0 - The coefficient of the predictor variable SCHOOLFEMALE is equal to zero against the
The alternative hypothesis
H1 – The coefficient of the predictor variable SCHOOLFEMALE is not equal to zero.
Interpretation of the results.
The coefficient of the variable SCHOOLFEMALE is -0.355 and the constant value is 14.012. The standardized regression equation is identical to the equation of a straight line and as such the gradient is represented by the coefficient of the independent variable (SCHOOLFEMALE) and the constant represents the y-intercept 14.012. The standardized regression equation will, therefore, be
Unempfemale = 14.012 – 0.355 (SCHOOLFEMALE)
This is the equation that is used in the prediction of the female unemployment rate when the expected years of schooling for female is known.
In the model summary table R represents the correlation coefficient which is also presented by the beta value in the coefficients table. The correlation between the variable unempfemale and the variable SCHOOLFEMALE is negative which shows that an increase in the independent variable leads to a decrease in the dependent variable. The value of the correlation coefficient indicate that the correlation is weak because the value is less than 0.2. A strong correlation between variables can generally be considered to be between 0.7 and 1. The value R2 is the coefficient of determination and it is a measure of the amount of variance of the outcome variable (Unempfemale) that can be predicted by the variance of the predictor variable (SCHOOLFEMALE).
Based on the results of analysis, we fail to reject the null hypothesis at 0.05 level of significance because the p-value = 0.219 > 0.05. Therefore, we conclude that the coefficients of the predictor variable are significantly equal to zero. As such the standardized regression equation is not appropriate for computing the outcome variable because the regression analysis was statistically insignificant.