From the last blog we saw he values obtained after performing linear regression is shown below :
The above values indicates:
The linear regression model indicates that, for every one-unit increase in %inactive:
– Slope (Coefficient for %inactive): %diabetic is expected to increase by approximately 0.23.
– Intercept: When %inactive is zero, the predicted %diabetic is around 3.77.
– R-squared value: The model explains about 19.51% of the variation in %diabetic.
– P-value: The very low p-value (1.63e-66) suggests a statistically significant relationship.
– Standard Error: The standard error (0.0128) reflects the precision of the model’s predictions.
In conclusion, the model indicates that %inactive and %diabetes have a statistically significant association. But the R-squared value shows that the model only partially explains the variance in %diabetes, indicating that %diabetic may be influenced by other factors not included in the model.