How to interpret this Residuals vs. Fitted Plot How to Check the non-constant variance using this ’residuals vs fitted values’ plot?
Measures of residuals heteroscedasticity This wikipedia  lists a number of techniques to detect OLS residuals heteroscedasticity. I would like to learn which hands-on technique is more efficient in detecting regions affected by heteroscedasticity.  For example, here the central region in the OLS 'Residuals vs Fitted' plot seen to have higher variance than the sides of the plot (I am not entirely sure in facts, but let's assume it's the case for the sake of the question). To confirm, looking at the error labels in the QQ plot we can see that they match the error labels in the center of the Residuals plot.  But how can we quantify the residuals region that has significantly higher variance?
Intuitive explanation of convergence in distribution and convergence in probability What is the intuitive difference between a random variable converging in probability versus a random variable converging in distribution?  I've read numerous definitions and mathematical equations, but that does not really help. (Please keep in mind, I am undergraduate student studying econometrics.)  How can a random variable converge to a single number, but also converge to a distribution?