Regression analysis is a statistical technique used to determine the linear relationship between the dependent and independent variables (Chatterjee, & Hadi, 2012). For the purpose of this study, the number of visits is the dependent variable while the distance to the nearest source of care is the independent variable. From the excel output, the coefficient of determination (R squared) is 0.0026 (0.26%), this means that 0.26% of the total variation is explained by the model while 99.74% represents the unexplained variation. This clearly shows that the model is not a good fit for the data.
The square root of the coefficient of determination is the correlation coefficient which is 0.051, this shows a weak positive correlation between the number of miles and distance.
From the ANOVA table, the value of F is obtained by dividing MSR by MSE. F = 0.046713 is then compared with the critical value with (1, 18) degrees of freedom. The p-value is 0.8313 is greater than the significance level of 0.05 which means that we fail to reject the null hypothesis and conclude that slope (β1) = 0.
The significance of the intercept and the slope is determined using the p-values and the significance level of 0.05, from the regression results the intercept has a p-value of 0.000298 while the slope has a p-value of 0.83132. The intercept is statistically significant since it has a p-value less than 0.05, the slope on the other hand is not statistically significant because it has a p-value greater than 0.05.
Chatterjee, S., & Hadi, A. S. (2012). Regression Analysis by Example. Hoboken: Wiley.
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