Residual plot of %Diabetes vs %Inactive
A residual plot is a graphical representation of the residuals (the differences between observed and predicted values) in a regression analysis. Examining the residual plot can provide insights into the validity of assumptions and the overall performance of the regression model.
Residual plot of %Diabetes vs %Inactive:
The red dotted horizontal line indicates the zero residual line and blue dots across the line indicates the residuals. With the above residual plot I am able to verify Homoscedasticity: Homoscedasticity means that the spread of residuals is constant across all levels of the independent variable.
linearity in the data: A good regression model assumes that the relationship between the independent and dependent variables is linear. In a residual plot, you ideally want to see a random, patternless spread of points around the horizontal axis. If there’s a clear pattern, it suggests that the relationship might not be entirely linear, and the model might need adjustment.
presence of outliers: Residual plots can help identify outliers, which are data points that deviate significantly from the overall pattern. Outliers can have a substantial impact on regression results.
and model fit assesment etc.